Agentic AI systems go beyond answering questions. An AI agent can plan steps, use software tools, and work towards a goal with limited supervision. That raises the stakes: a weak instruction can cause a wrong action, not just a wrong answer. This guide covers what to look for, what to ask, and how to compare your hiring options.

What Does a Prompt Engineer Do in an Agentic AI Project?

A prompt engineer writes and tests the instructions that tell an AI agent its job, which tools it may use, when to stop, and how to format its answers. It is one layer of the work. Context engineering covers what information the model sees at each step, such as retrieved documents, memory, and tool results. Broader AI engineering adds software development, orchestration, security, and monitoring.

Take a logistics company that wants an agent to read customer emails, check order status, and draft replies. The prompt engineer sets the agent’s tone and limits, decides when it must hand over to a person, and builds test cases such as angry customers or missing order numbers. A developer connects the order system. Neither role alone gets the agent into production.

McKinsey’s State of AI report (November 2025, survey run June–July 2025) found 62% of respondents’ organisations were at least experimenting with AI agents, while 23% were scaling an agentic system somewhere in the business. 

Gartner forecast in June 2025 that over 40% of agentic AI projects will be cancelled by the end of 2027, citing rising costs, unclear value, and weak risk controls. That is a prediction, not a result, but it shows why careful hiring matters.

What Skills Should You Look for in a Prompt Engineer in 2027?

No candidate needs every tool. Look for depth in these five areas.

1. Strong Understanding of LLMs and Prompt Design

Large language models (LLMs) are the AI models behind most agents. A strong candidate explains why a prompt fails and how they fixed it. Ask them to walk through a real prompt revision.

2. Experience With AI Agents, Tool Calling, and APIs

Tool calling lets an agent trigger actions, such as searching a database. Ask what happened when a tool returned an error and what they changed.

3. Context Engineering, RAG, and Knowledge Retrieval

Retrieval-augmented generation (RAG) lets a model look up company documents before answering. Check that they know when retrieval helps and when a better prompt is enough.

4. Prompt Testing, Evaluation, and Error Handling

Look for test sets, pass-or-fail criteria, and logs of failures they have fixed.

5. AI Security, Guardrails, and Human Oversight

Prompt injection means hidden instructions that trick an agent into misbehaving. Ask how they limit permissions and decide which actions need human approval.

How to Hire the Right Prompt Engineer for Your Agentic AI Project?

1. Define Your AI Agent’s Goals and Responsibilities

Write down the task, the tools involved, the data it can touch, and what it must never do.

2. Review Relevant Projects and Technical Portfolios

Ask what the candidate personally built, what failed, and how they measured improvement. Tool lists and certificates say little about actual contribution.

3. Test Candidates With a Small, Relevant Assignment

Give a short, paid task, such as improving a prompt against ten sample cases.

4. Evaluate Communication, Testing, and Problem-Solving Skills

Notice whether they ask about edge cases and explain trade-offs plainly.

5. Start With a Defined Scope and Measurable Deliverables

Agree on outputs such as a tested prompt set, an evaluation report, and documentation.

What Questions Should You Ask Before Hiring a Prompt Engineer?

• How would you evaluate an agent’s reliability? Strong answers mention test cases, success criteria, and repeated runs.
• How do you handle incorrect tool calls? Look for validation, retries, and safe fallbacks.
• When would you use RAG instead of changing a prompt? The answer should separate missing knowledge from unclear instructions.
• How would you test before release? Expect staged rollout and monitoring.
• How do you manage sensitive data and prompt injection? Listen for least-privilege access and human approval for risky actions.

How Much Does It Cost to Hire a Prompt Engineer for Agentic AI?

Cost depends on seven main factors: the candidate’s experience, project complexity, the number of systems the agent must connect to, how much testing is needed, security requirements, location, and engagement model.

A simple prompt-tuning task for one chatbot costs far less than a multi-step agent that reads private data and takes actions. Extra evaluation and security work adds time, but it also reduces the chance of an expensive failure after launch.

Published figures vary widely between sources, regions, and pay types, and hourly rates, salaries, and agency fees are not comparable. This guide therefore avoids a price range. Ask each candidate for a written quote that states scope, testing, revisions, and what happens if requirements change.

How Can Businesses Find a Suitable Freelance Prompt Engineer?

Start by shortlisting three to five candidates, reviewing their relevant work, and agreeing on deliverables and evaluation criteria before anyone begins. You can browse prompt engineers on WorkFlexi, or explore a generative AI engineer if your project needs wider model development. For broader data and model work, see WorkFlexi’s machine learning experts. Businesses new to the platform can also read how to hire freelancers in India. Review each profile yourself and run your own assignment.

 FAQs

1. What does a prompt engineer do for an AI agent?

A prompt engineer writes and tests the instructions that guide an AI agent: its role, the tools it can use, the limits it must respect, and when to ask a human. They also build test cases to check that the agent behaves consistently. They usually work alongside developers who connect the agent to business systems.

2. How do I hire a prompt engineer for an agentic AI project?

Write down your agent’s goal, tools, data access, and limits first. Then shortlist candidates with relevant, tested work, set a short paid assignment, and compare results. Agree on deliverables, such as a tested prompt set and an evaluation report, before the full project starts.

3. What skills should an agentic AI prompt engineer have?

Look for strong prompt design, experience with tool calling and APIs, an understanding of context engineering and RAG, and a habit of testing and evaluating outputs. Awareness of AI security, including prompt injection and human approval steps, matters too. No candidate needs every tool or framework.

4. Is prompt engineering enough to build an AI agent?

Usually not. Prompt engineering shapes how an agent behaves, but a working agent also needs software development, system integration, testing, security controls, and monitoring. Smaller projects may need only one skilled person, while complex ones often need a prompt engineer working with developers.

How much does it cost to hire a prompt engineer?

It depends on experience, project complexity, integrations, testing and security needs, location, and whether you hire a freelancer, employee, or agency. Published rates vary widely, and hourly rates, salaries, and agency fees are not directly comparable. Request a written quote that lists scope, testing, and revisions.

What is the difference between prompt engineering and context engineering?

Prompt engineering focuses on the instructions given to the model. Context engineering focuses on everything the model sees when it answers, including retrieved documents, memory, and tool results. In agent projects the two overlap, because good instructions still fail if the agent receives the wrong information.

How can I test a prompt engineer’s skills before hiring?

Give a small, paid assignment based on your real use case, such as improving an agent’s instructions against a set of sample situations. Review how they define success, handle edge cases, and report results. Their reasoning and testing method tell you more than a list of tools.

Sources

• Google, Creating helpful, reliable, people-first content: https://developers.google.com/search/docs/fundamentals/creating-helpful-content
• Google, Using generative AI content on your website: https://developers.google.com/search/docs/fundamentals/using-gen-ai-content. Search Engine Roundtable reports a 1 Oct 2026 update adding a “manually factcheck and review” instruction. I could not open the official page directly, so confirm the wording there: https://seroundtable.com/google-updates-ai-content-guidelines-factcheck-review-42217.html
• McKinsey, The state of AI in 2025: Agents, innovation, and transformation (November 2025; survey of 1,993 respondents, 25 June–29 July 2025): https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-state-of-ai-2025
• Gartner press release, 25 June 2025 (forecast, not a result): https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027

The data science trends most relevant to businesses in 2027 are generative AI for data exploration, AI agents for multi-step analysis, predictive and prescriptive analytics, real-time analytics, automated machine learning, and stronger data governance. You do not need all of them. Choose based on your goals, the data you already have, your budget, and your technical readiness.

Businesses already use data to understand customers, forecast demand, control costs, and make decisions. What is changing is how much of that work AI tools can speed up, and how much care is needed to keep the results accurate. Adoption is wide but uneven. 

McKinsey’s State of AI survey (published November 2025; 1,993 respondents, June–July 2025) found that 88% reported regular AI use in at least one business function. Yet only 39% attributed any EBIT impact to AI, and most of those said it was under 5%. Using a tool is not the same as getting business value.

What Are the Most Important Data Science Trends in 2027?

Several data science trends are shaping how businesses analyse information and make decisions. Predictive analytics is already well established and helps businesses forecast sales, understand customer behaviour, and plan demand. Generative AI is increasingly used to draft reports, summarise information, and explore data using everyday language, although its outputs still need to be checked for accuracy. Businesses can also explore generative AI solutions to support their data-related workflows.

AI agents are an emerging area of development. These systems can use tools and perform multiple steps to help complete tasks, such as gathering information or assisting with analytical workflows. However, they require appropriate permissions, thorough testing, and human oversight to reduce errors and manage risks.

Real-time analytics is another important trend because it allows businesses to analyse data as it arrives and respond to changes in customer activity or business performance. Automated machine learning can also help speed up selected stages of model development, such as testing different approaches, but human expertise remains important for evaluating results and choosing suitable models.

Finally, responsible AI and data governance are becoming increasingly important as businesses rely more on data-driven systems. Maintaining data quality, protecting sensitive information, controlling access, and assigning clear responsibility can help businesses use these technologies more reliably. While these developments are relevant to business planning for 2027, their adoption and impact will vary depending on each organisation’s needs, resources, and technical readiness.

How Is Generative AI Changing Data Science?

Generative AI tools, built on large language models (LLMs), let people work with data in plain language. An analyst can ask a question, get a draft SQL or Python query, and receive a first-pass summary of the findings. A retail manager might ask for last quarter’s sales by region and get a draft chart to refine.

The limits matter. These tools can give wrong answers with complete confidence. Sensitive customer or financial data should not go into a tool without approval. A person should check every figure before it informs a decision.

How Are AI Agents Changing Business Data Analytics?

An AI agent is software that can plan steps and use tools, such as querying a database and then drafting a summary. A weekly sales report is a simple example: the agent pulls the numbers, compares them with last week, and writes a first draft.

This area is still emerging. Agents need access limited to what the task requires, reliable data, testing before launch, monitoring afterwards, and human sign-off on important outputs. They are not ready or necessary for every business.

Why Are Predictive and Prescriptive Analytics Important for Businesses?

Predictive Analytics for Sales and Demand Forecasting

Predictive analytics uses past data to estimate what is likely to happen. A shop might forecast demand for next month to plan inventory, or flag customers who are likely to stop buying so the team can act early.

Prescriptive Analytics for Better Business Decisions

Prescriptive analytics goes one step further and recommends an action. Instead of saying “demand will rise 10%”, it suggests how much stock to order given costs and storage limits. It works only when the underlying predictions are trustworthy, so most businesses should master prediction first.

How Are Real-Time Analytics and Automated Machine Learning Evolving?

Real-time analytics means analysing data as it arrives. An online store could spot a sudden drop in completed checkouts within minutes rather than at month end. It depends on data pipelines that run reliably, and it can cost more than regular reporting, so reserve it for decisions that genuinely cannot wait.

Automated machine learning (AutoML) automates steps such as trying different models. It can give a useful baseline quickly. It does not remove the need for expertise. Someone still has to define the right question, check that the model did not learn from information it would not have in real use, and judge whether the result fits the business problem.

Why Are Data Quality, Governance, and Responsible AI Becoming More Important?

Poor data produces misleading analysis. Duplicate customer records, for instance, can inflate customer counts and make a churn model look more accurate than it is.

Gartner’s February 2025 press release predicted that through 2026, organisations will abandon 60% of AI projects not supported by AI-ready data. That is a forecast, not a result. Its July 2024 survey of 1,203 data management leaders found that 63% either lacked the right data management practices for AI or were unsure whether they had them. The practical lesson is to audit your data before buying more tools.

Governance means deciding who can access which data, how privacy is protected, how models are monitored after launch, and how bias is checked.

How Can Small Businesses Benefit From Data Science in 2027?

Small businesses do not always need a complex AI system. Often a clean spreadsheet, a SQL query, and a clear dashboard are enough to:

• Understand customer behaviour
• Improve sales and demand forecasts
• Identify customers who may stop buying
• Reduce avoidable operational costs
• Improve marketing and product decisions

What Skills Should Businesses Look for in Data Science Experts?

Look for Python, SQL, statistics, machine learning, data visualisation, model evaluation, and, just as important, the ability to explain results in plain business language.

The right profile depends on the problem. A data analyst usually reports on what has happened. A data scientist builds models. A machine learning engineer puts models into production. A data engineer builds the pipelines that feed everything. Hiring the wrong role is a common and avoidable mistake.

Competition for talent may be rising. The US Bureau of Labor Statistics projects data scientist employment to grow 35% from 2025 to 2035, much faster than the average for all occupations. That is a US projection, but it suggests why defining your needs before you search saves time.

You can explore machine learning experts or generative AI engineers on WorkFlexi, and see how to hire freelancers in India. Review each profile against your own criteria.

How Should Businesses Prepare for Data Science in 2027?

1. Identify one clear business problem.
2. Review the quality and availability of the relevant data.
3. Choose an appropriate analytical approach.
4. Define measurable success criteria.
5. Start with a manageable pilot.
6. Review accuracy, costs, risks, and business value before expanding.

The best 2027 strategy is not to follow every trend. It is to pick the one or two that solve a real problem you already have, and to prove their value on a small scale first.

 FAQs

What are the biggest data science trends in 2027?

The most relevant trends are generative AI for data exploration, AI agents for multi-step analysis, predictive and prescriptive analytics, real-time analytics, automated machine learning, and stronger data governance. Which ones matter to you depends on your goals, data quality, budget, and technical readiness. Most businesses should start with one or two.

How is generative AI changing data science?

Generative AI lets people query and explore data in plain language, draft code, and summarise findings. It speeds up routine work. It can also produce confident but wrong answers, so a person must verify results, and sensitive data should only be used in tools your organisation has approved.

How can businesses use data science to improve decision-making?

Start with one clear problem, such as forecasting demand or spotting customers likely to leave. Check your data quality, choose a suitable method, and define how you will measure success. Run a small pilot, then review accuracy, cost, and value before expanding.

Will AI agents replace data scientists?

Agents can automate parts of analysis, such as pulling data and drafting reports. Framing the right problem, checking data quality, validating results, and explaining trade-offs still need human judgement. No one can say for certain how roles will change, but agents need oversight rather than working unsupervised.

How can small businesses benefit from data science?

Small businesses can use data to understand customer behaviour, improve forecasts, spot customers who may stop buying, and cut avoidable costs. Many of these start with a clean spreadsheet, SQL, and a simple dashboard. A complex AI system is often unnecessary at the start.

What skills should a data scientist have in 2027?

Core skills include Python, SQL, statistics, machine learning, data visualisation, and model evaluation. Business communication matters just as much, because findings must be understood by non-technical decision-makers. Needs differ by project: some call for a data analyst, others for a data scientist, machine learning engineer, or data engineer.

How can businesses hire a data scientist expert?

Define the business problem and the role you need first. Shortlist candidates with relevant, explained project work, then give a small paid task using your real data type. Agree on deliverables and success measures before the main work begins. Platforms like WorkFlexi can help you find freelancers.

Generative AI projects usually fail because of avoidable problems, not because the model is weak: the goal is vague, the data is poor, outputs are never properly tested, or nobody plans for production. Experienced developers reduce these risks by defining success early, grounding answers in trusted data, testing before launch and monitoring afterwards. 

They cannot guarantee success, but they can remove the most common mistakes. This guide explains those mistakes, how to prevent them, and when specialist help makes sense.

Why Do Generative AI Projects Fail?

Most failures happen between the proof of concept (PoC) and the live system. A PoC runs on tidy sample data and friendly questions. Production meets messy data, unusual requests, security rules and real costs.

RAND’s 2024 study, based on 65 interviews with experienced AI practitioners, cites estimates that more than 80% of AI projects fail, about twice the rate of IT projects without AI. Of the industry interviewees, 84% named leadership-driven causes, such as solving the wrong problem. RAND studied machine learning broadly, so treat this as a general signal, not a generative AI measurement. MIT’s Project NANDA 2025 report found that about 95% of enterprise generative AI pilots showed no measurable profit-and-loss impact, which points the same way.

7 Common Generative AI Project Mistakes and How Developers Prevent Them

1. Starting without a clear business objective.

 Without a target, nobody can tell whether the system works. Developers turn goals into measurable outcomes, such as shorter support handling time, before building. Illustrative scenario: a retailer launches a chatbot but never decides which questions it should resolve.

2. Using poor-quality, outdated or incomplete data. 

Large language models (LLMs) are the AI systems behind tools like chatbots, and they work only with what they are given. Developers audit sources, remove duplicates and schedule updates. Illustrative scenario: a help bot quotes a retired refund policy.

3. Ignoring AI hallucinations.

Hallucinations are confident but false answers; NIST calls them confabulation. Developers use Retrieval-Augmented Generation (RAG), where the model answers from approved documents, plus citations and fallback replies such as “I don’t have that information.”

4. Choosing the wrong model or technology stack. 

The largest model is not always the best fit; it may be slow or costly. Developers compare options on accuracy, speed and cost for the actual task.

5. Skipping testing and evaluation. 

Spot-checking a few answers hides failures. Developers build evaluation datasets of real questions with expected answers and rerun them after every change.

6. Overlooking privacy, security and compliance.

 Customer data can leak through prompts, logs or loose access settings. Developers apply access controls, mask sensitive data and guard against prompt injection, where hostile text tries to override the system’s instructions.

7. Underestimating integration, infrastructure costs and maintenance. 

Connecting to CRMs, databases and workflows takes time, and usage costs grow with traffic. Developers test integrations early, set usage limits and plan ongoing monitoring.

How Do Experienced Generative AI Developers Prevent Project Failure?

They follow a repeatable order: set measurable success criteria, choose the model and architecture, use RAG where answers must rely on trusted knowledge, build evaluation sets, add guardrails and human review for high-risk outputs, protect sensitive data, test integrations, then monitor accuracy, latency, cost and user feedback after launch.

NIST’s Generative AI Profile (AI 600-1, July 2024) lists 12 risks unique to or worsened by generative AI, including confabulation, data privacy and information security. It is a practical checklist for spotting implementation risks early.

How Can Businesses Measure Generative AI Project Success?

Match metrics to the use case. Common ones include task accuracy, groundedness (answers supported by source documents), hallucination rate, task completion, response latency, cost per request, user satisfaction and business outcomes such as resolution time. No single metric is enough: a fast, cheap assistant that is often wrong still fails.

When Should You Hire Experienced Generative AI Developers?

Specialist help suits complex integrations, RAG pipelines, production launches, evaluation frameworks, data privacy, AI agents and ongoing optimization.

When you hire Generative AI developers, ask for production examples, not only demos. Check Python and LLM API experience, RAG and vector database knowledge, testing habits, security awareness and how clearly they explain trade-offs.

Build Generative AI Solutions That Work in the Real World

Generative AI projects fail when teams skip the unglamorous work: clear goals, clean data, testing, security and monitoring. Before your next project, write down your success metric, list your data sources and decide how outputs will be tested. If you need extra capacity, you can explore independent Generative AI engineers on Workflexi.

Frequently Asked Questions

1. What are the biggest challenges in Generative AI development?

Common challenges include data quality, hallucinations, model selection, integration with existing systems, privacy and security, and running costs. They are linked: poor data raises hallucination risk, and weak testing hides it. Addressing them early is cheaper than fixing a live system.

2. How can businesses reduce hallucinations in Generative AI applications?

Ground answers in approved sources with RAG, require citations, limit the assistant’s scope and add fallback replies when information is missing. Test with real questions and have people review high-risk outputs. These steps lower the risk of hallucinations but cannot eliminate them entirely.

3. Why is data quality important for Generative AI projects?

A model can only work with what it receives. Outdated, duplicated or incomplete documents lead to wrong or inconsistent answers, even with a strong model. Regular audits and a named owner for each data source keep information accurate as the business changes.

4. How can developers test a Generative AI application before launch?

Build an evaluation set of realistic questions with expected answers, including edge cases and misuse attempts. Score accuracy, groundedness and refusals, then rerun the set after every change. Add human review for sensitive uses and run a limited pilot before wide release.

5. When should a business hire a Generative AI developer?

Consider hiring one when a project involves private data, system integrations, RAG, AI agents or customer-facing output, or when a PoC will not move to production. Small internal experiments, such as drafting routine content, may not need outside specialist support.

 Sources

• RAND, The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed (Aug 13, 2024): https://www.rand.org/pubs/research_reports/RRA2680-1.html. Opened directly.
• MIT Project NANDA, “GenAI Divide” report (2025). I could only confirm the 95% figure through secondary articles (e.g. https://www.pertamapartners.com/insights/ai-project-failure-statistics-2026), not the original report.
• NIST, Generative AI Profile, NIST AI 600-1 (July 2024): https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf. Confirmed through search results only.

1. Agentic AI Consulting Will Help Automate Complex Workflows

Agentic AI means systems that plan and complete multi-step tasks using approved tools. Hypothetical scenario: an agent reads a support ticket, checks the order system, drafts a reply and asks a person to approve it. Gartner forecast in 2025 that over 40% of agentic AI projects will be canceled by the end of 2027, citing cost, unclear value and weak risk controls. Consultants help pick suitable use cases and design permissions, testing and human approval.

2. AI Strategy Consulting Will Focus on Measurable ROI

Expect budgets to follow evidence. McKinsey’s 2025 State of AI survey found nearly two-thirds of respondents had not begun scaling AI across the enterprise, and only 39% reported any enterprise-level profit impact. Consultants help set KPIs before spending, such as processing time, cost per task, error rate and conversion, then judge a pilot against them.

3. Generative AI Consulting Will Move Beyond Chatbots

Businesses are shifting from generic chatbots to assistants connected to company data. Retrieval-augmented generation (RAG) lets a model look up approved documents before answering, which can help ground responses. Document processing, such as extracting fields from invoices, is a common first project. Generative AI engineers and consultants handle integration, accuracy testing, privacy and ongoing monitoring.

4. AI Governance Consulting Will Become a Baseline Need

Governance means rules for data access, risk review, human accountability and output monitoring. In the EU, the AI Act’s transparency duties under Article 50 have applied since August 2, 2026, while the Digital Omnibus moved stand-alone high-risk obligations to December 2, 2027. These are legal dates for EU-linked businesses; planning governance before deployment is recommended practice everywhere.

5. Industry-Specific AI Consulting Will Grow

Generic tools rarely fit regulated or specialized work. Healthcare needs privacy controls, finance needs audit trails, retail needs product and customer data, manufacturing needs sensor data, logistics needs route and inventory systems, and software teams need code and security checks. Data availability, compliance and existing systems shape what is realistic.

6. AI Implementation Will Prioritize Data Readiness and Integration

Fragmented or poor-quality data limits any model. Consultants assess data readiness, connect AI to existing software through APIs and data pipelines, then manage testing, deployment, staff training and monitoring. Deloitte found only 30% of organizations were redesigning key processes around AI, so many still layer AI onto old workflows.

7. AI Consulting Teams Will Pair Specialists With Human Oversight

Projects increasingly need AI engineers, machine learning specialists, data engineers, architects and domain experts, often for a limited time. Businesses can use external or flexible specialists for defined projects, while people keep accountability for decisions. When choosing, check relevant experience, communication and security practices. WorkFlexi is a marketplace where businesses can find freelance AI consultants and machine learning experts.

How Should Businesses Prepare for AI Consulting Trends in 2027?

1. Pick a business problem worth solving.
2. Check data quality and system readiness.
3. Rank use cases by value, feasibility and risk.
4. Define measurable success criteria.
5. Select suitable technical expertise.
6. Run a controlled pilot.
7. Monitor results and improve.

Successful AI adoption needs a clear strategy, reliable data, responsible governance, suitable expertise and measurable outcomes. AI consulting services can help at each step, from choosing a use case to monitoring results. If you need specialist support, explore AI and machine learning talent on WorkFlexi.

Frequently Asked Questions 

1. What are the major AI consulting trends for 2027?

Likely 2027 trends include agentic AI workflows, ROI-focused strategy, generative AI connected to company data, AI governance, industry-specific solutions, data readiness and integration, and specialist teams with human oversight. These are informed forecasts based on 2025–2026 research, not guaranteed outcomes.

2. What do AI consulting services include?

AI consulting services typically include opportunity assessment, data and readiness review, use-case prioritization, tool and vendor selection, implementation support, governance planning and results measurement. Some also build and deploy solutions. Scope varies by provider and project, so ask for deliverables in writing.

3. How can AI consulting services help businesses improve ROI?

They help define success metrics before spending, rank use cases by value and feasibility, and test a pilot against baseline numbers such as processing time, cost per task or error rate. That makes it easier to stop weak projects early and scale proven ones. Results are never guaranteed.

4. How much do AI consulting services cost?

There is no standard price. Cost depends on project scope, data readiness, integrations, the expertise required, location and whether the engagement is a short assessment or a full implementation. Request a scoped proposal with milestones, and compare freelance and agency options against your needs.

5. Why do businesses need AI strategy consulting before implementing AI?

Strategy work clarifies which problem to solve, whether data and systems are ready, and how success will be measured. Without it, projects risk stalling as pilots with unclear value, wasted spend or avoidable compliance problems. Starting small keeps these risks manageable.

6. What is the difference between AI consulting and AI implementation?

AI consulting focuses on strategy: finding opportunities, assessing readiness, choosing approaches and planning governance. AI implementation is the hands-on build: integrating models, connecting data, testing, deploying and monitoring. Some providers offer both, while others specialize, so confirm which stage you are buying.

7. How can a business choose the right AI consulting company?

Start with your goal, then check relevant project experience, how they test accuracy, data security practices and clarity of deliverables. Ask for references or examples, and be cautious of guarantees. A small paid pilot is a practical way to judge communication and quality before a larger commitment.

Sources

• Deloitte, “The State of AI in the Enterprise: The Untapped Edge” (2026 edition, released January 2026). The survey covered 3,235 leaders in 24 countries, fielded in August and September 2025. It supports the 74% figure for expecting at least moderate use of AI agents by 2027, and the 21% with a mature agent-governance model: https://www.deloitte.com/us/en/insights/topics/emerging-technologies/ai-agents-scaling-faster.html. Its press release supports the 30% redesigning key processes and 37% surface-level use figures: https://www.deloitte.com/nl/en/about/press-room/state-of-ai-in-the-enterprise-2026.html
• McKinsey & Company, “The state of AI in 2025: Agents, innovation, and transformation” (November 5, 2025). It supports “nearly two-thirds not yet scaling” and “39% report enterprise-level EBIT impact”: https://www.mckinsey.com/capabilities/operations/our-insights/the-state-of-ai
• Gartner, press release (June 25, 2025). It is a forecast that over 40% of agentic AI projects will be canceled by the end of 2027: https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027
• EU AI Act, Digital Omnibus on AI (Regulation (EU) 2026/1744), in force July 27, 2026. It supports the Article 50 and December 2, 2027 dates. I read two law-firm summaries: DLA Piper (https://knowledge.dlapiper.com/dlapiperknowledge/globalemploymentlatestdevelopments/2026/The-Digital-AI-Omnibus-Proposed-deferral-of-high-risk-AI-obligations-under-the-AI-Act) and Cooley (https://cdp.cooley.com/digital-ai-omnibus-delays-key-deadlines-introduces-new-rules/)

 This guide explains what a data scientist does in generative AI projects, which skills matter, where the work applies, how the role differs from a generative AI engineer, and how to choose the right person.

What Does a Data Scientist Expert Do in Generative AI Projects?

A data scientist expert uses data, statistics, and experiments to help a generative AI system deliver reliable results for a business goal. They do not always build the model. Often, they make sure it gets good data and is judged fairly.

Common responsibilities include:

• Collecting, cleaning, and analysing data
• Turning business requirements into measurable questions
• Preparing datasets for testing and retrieval
• Running experiments and comparing approaches
• Evaluating model outputs and spotting repeated errors
• Measuring performance over time
• Working with AI engineers and developers

The exact scope depends on the project and the organisation. In a small company, one person may cover many of these tasks. In a larger team, the data scientist may focus on analysis and evaluation.

Why Do Generative AI Projects Need Data Science Expertise?

Generative AI projects need data science expertise because output quality depends on data quality, and someone has to measure it. A data scientist can find gaps in source documents, analyse wrong answers, check whether the system retrieves the right information, and connect results to business outcomes.

Using AI is now common, but scaling it is harder. McKinsey’s 2025 global survey of 1,993 respondents found that 88% of organisations use AI in at least one business function and 79% use generative AI. Only 7% said AI was fully scaled across their organisation. This does not prove that one role closes the gap, but it shows why careful measurement matters.

Not every project needs a dedicated data scientist. A simple tool built on a ready-made product may need mainly software engineering, AI integration, data engineering, or prompt engineering skills.

What Skills Should a Data Scientist Expert Have for Generative AI?

Python, SQL, and Data Preparation

Python is the main language for data work. Pandas and NumPy help clean and analyse data, while SQL pulls it from databases. Together they help prepare datasets, find missing or duplicate records, and build test sets.

Statistics and Machine Learning

Statistical reasoning helps compare results fairly and avoid false conclusions. Machine learning basics, such as classification and model evaluation with Scikit-learn, help measure performance.

LLMs, Embeddings, and RAG

A large language model (LLM) is an AI model trained on large amounts of text to generate language. Embeddings turn text into numbers so similar meanings can be matched. Retrieval-augmented generation (RAG) lets an LLM look up relevant company documents before it answers.

A data scientist may prepare those documents, test how they are split, and measure whether the system finds the right passages, without building the whole system.

Generative AI Evaluation and Error Analysis

AI responses can be tested for relevance, factual consistency, retrieval quality, and task completion. A data scientist builds a test set of real questions, scores answers against clear criteria, and groups failures by cause, such as missing data or the wrong source. Human review remains important because automated scoring has limits.

Communication and Business Understanding

Findings must make sense to non-technical teams. A good data scientist defines success in business terms, such as the share of customer questions answered correctly from approved documents.

What Are the Business Applications of Data Scientists in Generative AI?

Here are practical examples. Each shows the problem, the data scientist’s contribution, and the intended outcome.

• Customer support assistants: Teams answer the same questions repeatedly. A data scientist studies past tickets, builds test questions, and measures answer quality. The goal is consistent replies, with hard cases passed to people.
• Internal knowledge search: Staff struggle to find policies. A data scientist checks document quality and retrieval accuracy. The goal is faster answers that point to their sources.
• Document summarisation and extraction: Contracts and reports take time to read. A data scientist compares outputs with human-checked samples to find error patterns. The goal is draft summaries that people can review.
• Customer feedback analysis: Reviews and survey comments are hard to read at scale. A data scientist groups themes and checks labels. The goal is a clearer view of customer needs.
• AI-assisted business reporting and analytics: Leaders ask questions in plain language. A data scientist validates answers against source data. The goal is reports people can trust.
• Personalised recommendations: Suggestions may not match user behaviour. A data scientist tests variations and measures results. The goal is more relevant suggestions.

When Should a Business Hire a Data Scientist Expert for a Generative AI Project?

Consider hiring one when you need complex data analysis, systematic AI evaluation, structured experiments, retrieval-quality analysis, or insights from large datasets.

Another specialist may fit better in some cases:

• ML engineer: for training and deploying custom models
• Data engineer: for pipelines, storage, and data access
• Generative AI engineer: for building the application itself
• Prompt engineering expert: for improving instructions and output behaviour

How Can You Choose the Right Data Scientist Expert?

Use this checklist:

• Relevant project experience
• Strong Python and SQL skills
• Knowledge of data preparation and statistics
• Understanding of generative AI evaluation, including how they would test answer quality
• Ability to explain technical findings in plain language
• Awareness of data privacy and security
• A portfolio or demonstrable work
• Ability to set measurable project objectives

WorkFlexi is one platform businesses can explore when looking for freelance data scientists. If your project also needs building or prompt design skills, you can look at generative AI developers or prompt engineers, or browse AI and machine learning professionals.

A data scientist expert helps generative AI projects by improving data, running experiments, and checking whether outputs are accurate and useful. The right professional depends on your objectives, data needs, system design, and evaluation requirements. If you are ready to hire a freelancer, WorkFlexi can be a place to start your search.

 FAQs

1. What does a data scientist expert do in generative AI projects?

A data scientist expert prepares and analyses data, designs tests for AI outputs, studies errors, and measures performance against business goals. They often work alongside AI engineers and developers. Their exact role depends on the project, and they do not always build the model or the full application themselves.

2. What skills does a data scientist need for generative AI?

Key skills include Python, SQL, Pandas, NumPy, statistics, and machine learning basics. For generative AI, they should also understand LLMs, embeddings, and RAG, plus evaluation and error analysis. Clear communication matters too, because findings must guide business decisions.

3. Can a data scientist work with large language models?

Yes. Data scientists often work with LLMs by preparing data, building test sets, checking retrieval quality, and evaluating answers. Some also build prototypes or fine-tune models, but many focus on analysis and measurement while engineers handle deployment. The split depends on the team and the project.

4. What is the difference between a data scientist and a generative AI engineer?

A data scientist mainly analyses data, runs experiments, and evaluates results. A generative AI engineer mainly builds and deploys applications that use LLMs, such as RAG systems and API integrations. The roles overlap, and in smaller teams one person may do both.

5. How does a data scientist evaluate AI-generated responses?

They create a test set of realistic questions, then score answers for relevance, factual consistency, and task completion. For RAG systems, they also check whether the right documents were retrieved. Human reviewers handle unclear cases, because automated scoring cannot catch every error.

6. When should a business hire a data scientist for an AI project?

Hire one when you need systematic evaluation, structured experiments, retrieval-quality analysis, or insights from large datasets. If you mainly need to build the application, an AI engineer may fit better. A data engineer suits data pipeline problems, and a prompt engineer suits instruction design.

7. What tools does a data scientist use in generative AI projects?

Common tools include Python, SQL, Pandas, NumPy, Scikit-learn, and Jupyter notebooks. For generative AI work, they may also use LLM APIs, Hugging Face libraries, cloud platforms, and evaluation scripts. Teams choose tools based on the project, the data, and existing systems.

 Sources and References

• McKinsey, “The state of AI in 2025: Agents, innovation, and transformation” (Nov 5, 2025): 88% use AI in at least one function and 7% fully scaled. https://www.mckinsey.com/featured-insights/charts/ai-at-work-but-not-at-scale
• The same McKinsey survey (1,993 participants, June 25–July 29, 2025): 79% use generative AI, per the chart on the page above.
• Google Search Status Dashboard: https://status.search.google.com/summary
• March 2026 core update: https://status.search.google.com/incidents/7eTbAa2jWdToLkraZj5y
• August 2026 spam update: https://status.search.google.com/incidents/LEubPCm2octf2uMqCFKE

Agentic AI describes AI systems that work toward a goal across several steps, instead of answering one prompt and stopping. An AI agent can plan, use approved tools such as a CRM or knowledge base, check results, and decide what to do next. Building one that works reliably takes more than a good prompt. It needs integrations, testing, and human oversight where mistakes are costly. Many businesses bring in freelance AI experts for that specialized work, often starting with a small pilot.

What Is Agentic AI and How Does It Work?

Agentic AI is a way of building systems where a language model works toward a goal by choosing actions, using tools, and reacting to results. An agent typically receives a goal, plans steps, calls approved tools through APIs, observes what happened, and decides what to do next. Memory or state tracks progress, and orchestration code manages the flow.

Anthropic’s engineering guidance separates workflows, where code sets the steps, from agents, where the model directs its own steps and tool use. The difference from a chatbot is action: a chatbot answers, while an agentic workflow can retrieve, draft and update. Generative AI is often one component inside it.

Example: an agent retrieves information from an approved knowledge base, drafts a customer reply, and sends it to a person for approval. Not all agents are fully autonomous. Their capabilities depend on design, permissions, and tools.

Why Do Businesses Hire Freelance AI Experts for Agentic AI Projects?

Businesses hire freelance AI experts to get specific skills for a defined project without a permanent hire. Common reasons include building and testing a proof of concept, integrating agents with existing software and APIs, improving workflows through controlled automation, and evaluating reliability and security. A freelancer can also fit when you want to learn what an agent can do before building a larger team. Whether a freelancer, consultant, or full-time team fits depends on complexity, budget, security requirements, and long-term support needs.

What Skills Should a Freelance Agentic AI Expert Have?

Focus on the skills that matter most for business projects:

1. Python Programming and API Integration

Python is a common choice for agent work. APIs, authentication and webhooks let an agent read from and update business apps under controlled permissions.

1. Large Language Models and Prompt Engineering

Experts choose models, request structured outputs, and design tool calling and instructions. Prompt engineering alone is not enough, because reliability also depends on testing, guardrails and fallbacks.

1. AI Agent Frameworks and Workflow Orchestration

LangChain offers prebuilt agent loops, while LangGraph is a lower-level orchestration framework for long-running, stateful agents with human-in-the-loop support, per LangChain’s documentation. The right choice depends on the use case, and Anthropic advises starting with the simplest approach that works.

1. Retrieval-Augmented Generation and Knowledge Retrieval

RAG lets an agent look up relevant passages in approved company documents before answering, which helps ground responses in your own data.

1. Testing, Security and Human Oversight

Look for evaluation, logging and monitoring, access controls, protection against prompt injection (malicious instructions hidden in content), data privacy, and approval steps for sensitive actions.

What Are the Business Applications of Agentic AI?

These are illustrative examples, not customer case studies.

1. Customer Support and Service Operations

An agent classifies a request, retrieves approved answers, drafts a reply, and escalates complex or sensitive cases to a person.

1. Research, Reporting and Data Analysis

An agent gathers permitted sources, organizes findings, and drafts a report. A person should verify key facts before it is shared.

1. Sales and Lead Management

An agent qualifies inbound leads, updates CRM fields, and prepares follow-up drafts. Write access to the CRM should be limited, and reps should review outreach.

1. Business Process and Workflow Automation

An agent coordinates multi-step tasks across approved apps, such as intake, document checks and status updates, and tracks progress. Steps with financial or legal impact need approval.

1. Internal Knowledge Assistants

An agent helps employees find answers across authorized documents. Test that it respects each person’s access rights.

What Are the Benefits and Challenges of Agentic AI for Businesses?

Evidence suggests caution. McKinsey’s 2025 State of AI survey found 62% of respondents at least experimenting with AI agents and 23% scaling an agentic system somewhere, but no more than 10% scaling agents in any single function.

Gartner predicted in June 2025 that over 40% of agentic AI projects will be canceled by the end of 2027 because of costs, unclear value or weak risk controls. That is a forecast, not a measured result. Outcomes depend on the use case, data and ongoing evaluation.

How Can You Hire the Right Freelance AI Expert for an Agentic AI Project?

1. Define the business problem and the outcome you want.
2. Decide whether you need basic automation, an AI assistant, or a multi-step agentic workflow.
3. Review the expert’s portfolio and relevant project experience.
4. Ask how they test accuracy, reliability, security and failure cases.
5. Agree on deliverables, scope, milestones, timelines and budget.
6. Discuss data access, intellectual property, documentation, maintenance and support.
7. Consider starting with a limited proof of concept before expanding.

WorkFlexi is a freelance talent marketplace where you can explore freelance AI experts, generative AI engineers and prompt engineers for projects like these. WorkFlexi’s guide to AI agent frameworks can also help you prepare your brief.

Is Agentic AI the Right Choice for Your Business?

It may be, when a process has many steps, uses several tools, and needs judgment on unstructured information. For a fixed, predictable process, conventional automation or a simple workflow is often simpler and easier to control. Clear goals, suitable expertise, limited permissions, testing and human oversight matter either way. If you are ready to scope a project, explore relevant freelance AI talent through WorkFlexi, or start with one of its AI consultants.

FAQs

1. What is the difference between agentic AI and generative AI?

Generative AI creates content, such as text or images, from a prompt. Agentic AI uses a language model to pursue a goal across several steps, calling tools and reacting to results. Generative AI is often one component inside an agentic system. A drafted email is generative; an agent that finds the customer record, drafts the email and logs it is agentic.

2. What skills should an agentic AI developer have?

An agentic AI developer needs Python, API integration, experience with language models and tool calling, workflow orchestration, and retrieval (RAG) for company knowledge. Equally important are testing, security awareness and designing human approval steps. Strong candidates can explain why they chose a design and how they handle failures.

3. Which tools are used to build AI agents?

Common tools include Python, agent frameworks such as LangChain and LangGraph, model APIs from providers like OpenAI or Anthropic, vector databases for retrieval, workflow automation tools, and evaluation and monitoring tools. A project rarely needs all of them. The right mix depends on the workflow, security needs, budget and existing systems.

4. How can businesses use agentic AI?

Businesses use agentic AI for customer support triage, research and reporting, lead qualification, multi-step workflow automation, and internal knowledge assistants. A typical example is an agent that retrieves approved information, drafts a reply and asks a person to approve it. Sensitive actions should keep human review.

5. When should a business hire a freelance AI expert for agentic AI?

Consider a freelance AI expert when you need a specific skill for a defined project, such as a proof of concept, an integration with existing software, or an evaluation of an existing agent. A full-time team may suit long-term products with heavy security or support needs. Start small, then decide.

6. How much does it cost to develop an AI agent?

There is no standard price. Cost depends on scope, integrations, data preparation, the number of tools and approval steps, testing, and ongoing monitoring. A narrow proof of concept usually costs less than a production system connected to several business apps. Ask for a scoped quote with milestones, and budget for maintenance and model usage fees.

7. What are the main challenges of implementing agentic AI?

Main challenges of agentic AI include unclear goals, integration effort, data permissions and privacy, unreliable outputs, security risks such as prompt injection, and ongoing monitoring. A small pilot with clear success measures and human approval for sensitive actions helps manage these risks.

7. Sources

• McKinsey & Company, “The state of AI in 2025: Agents, innovation, and transformation” (November 5, 2025): https://www.mckinsey.com/capabilities/operations/our-insights/the-state-of-ai. It supports the 62% experimenting, 23% scaling, and “no more than 10% in any individual function” figures.
• Gartner, press release “Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027” (June 25, 2025): https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027. This is a forecast, and the article labels it as one.
• Anthropic, “Building effective agents” (December 2024): https://www.anthropic.com/engineering/building-effective-agents. It supports the workflow-versus-agent distinction and the advice to start simple.
• LangChain documentation, “LangGraph overview”: https://docs.langchain.com/oss/python/langgraph/overview. It supports the descriptions of LangChain and LangGraph.

Prompt engineering for business means designing and testing the instructions, context and output formats an AI model receives so it handles a specific task reliably. Experts build better AI workflows by defining success, designing prompts with the right context, requiring structured outputs, testing on real cases, adding human review and connecting results to business tools. It improves consistency in support, marketing, research and operations. Not every business needs a dedicated prompt engineer: occasional personal use rarely does, while team-wide, API-connected or high-risk uses often do. 

Most businesses have tried an AI chatbot by now. The harder part is getting reliable results from it every day, on real tasks, with real data. That is where prompt engineering for business comes in. Stanford’s 2026 AI Index reports that 70% of surveyed organisations used generative AI in at least one business function in 2025.

 Yet McKinsey’s early-2025 report found that only 21% of respondents whose organisations used generative AI said they had fundamentally redesigned at least some workflows. Using AI and building it into the work are different things. This article explains how experts close that gap.

What Is Prompt Engineering for Business?

Prompt engineering for business is the practice of designing, testing and improving the instructions, context and output formats given to an AI model so it performs a specific business task reliably. It goes beyond typing a question into a chatbot. A prompt engineer defines what the model should do, what information it gets, and what a good answer looks like.

Compare two requests. “Summarise this email” can give a different result each time. A designed prompt names the audience, supplies company policy, requires a fixed format, and says what to do when information is missing. That consistency is what lets a team repeat a task.

How Can Prompt Engineering Improve Business Workflows?

It improves workflows by making AI output more consistent, easier to check and easier to connect to other tools. Common uses include:

• Customer support: drafting replies from approved policy
• Marketing: product descriptions and campaign ideas in a set brand voice
• Research and reporting: summarising long documents into a fixed template
• HR: answering staff policy questions and drafting job descriptions
• Operations: extracting fields from invoices or forms

People stay in charge. AI drafts and sorts, and staff review decisions that carry risk. Stack Overflow’s 2025 survey found 46% of developers do not trust the accuracy of AI output, which is why review steps matter.

What Does a Prompt Engineer Expert Actually Do?

A prompt engineer expert turns a business task into a reliable AI process. They clarify the goal, design prompts, define inputs and outputs, test against real examples, and fix inconsistent answers. They also create reusable templates, connect prompts to LLM APIs and tools, evaluate outputs, and work with developers and business teams so the result fits daily operations.

How Do Experts Build Better AI Workflows?

They follow a repeatable loop: business goal, task analysis, prompt design, context, output format, testing, evaluation, workflow integration, human review, improvement.

1. Goal and task analysis: Decide what success looks like, such as “reply drafts need only light edits.” Break the task into steps.
2. Prompt design and context: Write clear instructions and supply the right information, such as policy text, examples or retrieved documents (RAG).
3. Output format: Ask for structured outputs like JSON or fixed fields so other software can use the result.
4. Testing and evaluation: Run the prompt on real cases and score the results. Anthropic’s documentation advises defining success criteria and testing against them before tuning prompts.
5. Integration and review: Connect the prompt to your tools, and add human review where mistakes are costly.
6. Improvement: Track failures and refine the prompt.

Example: AI Customer Support Workflow

Customer query → context retrieval → prompt → LLM → structured response → human review or automation → customer.

At each stage the prompt engineer contributes:

• Retrieval: decides which help articles and policies the model sees
• Prompt: sets tone, rules and what to do when unsure, such as escalating
• Structured response: requires fields such as answer and source
• Review: sets rules for which replies send automatically and which go to staff

Short Example: Marketing

A prompt engineer builds a template for product descriptions that holds brand voice, banned claims and length, and requires a fixed format. A marketer reviews each draft, which keeps speed without losing control.

What Makes a Business AI Workflow Effective?

Effective workflows have clear objectives, good input data, well-designed instructions, a consistent output format, testing, human oversight, privacy safeguards and integration with existing tools. Most important, they have measurable outcomes, such as time saved per ticket or edit rate. Without a measure, nobody knows whether the AI helps. Never paste sensitive customer data into tools your company has not approved.

Prompt Engineering vs AI Automation: What’s the Difference?

Prompt engineering shapes how an AI model responds. AI automation connects steps and tools so work runs with little manual effort. Good automation often depends on good prompts, so they work together. The table below compares them.

Think of it as a chain: prompt engineering shapes how an LLM responds; workflow design arranges those responses into steps; automation and AI agents run the steps; business outcomes come from the result.

When Should a Business Hire a Prompt Engineer Expert?

Hire one when AI output must be consistent, accurate and repeatable across a team. Typical signs: results vary between staff, prompts must connect to APIs or company data, errors carry compliance or brand risk, or you plan to build chatbots or agents.

You may not need one if a few people use AI occasionally for personal drafting, or if a ready-made tool already works well. Anthropic’s guidance also notes that not every failing result is solved by prompt engineering. Sometimes the data, the model or the process needs fixing instead.

What Skills Should a Business Look for in a Prompt Engineering Expert?

Look for people who can explain why they made each choice. Key skills and why they matter:

• LLM understanding: knowing model limits prevents unrealistic promises
• Prompt design and testing: reliability comes from testing, not luck
• Structured outputs and API knowledge: needed to connect AI to software
• RAG fundamentals: lets the model use your documents
• Workflow and agent design: breaks work into safe steps
• Evaluation: proves quality instead of guessing
• Business-process understanding and communication: ties AI work to real outcomes

Ask for examples of tested prompts and how they measured results.

Finding Prompt Engineering Expertise

If you need specialised help, WorkFlexi is a freelance marketplace where businesses can hire prompt engineers, find generative AI developers and work with wider AI and machine learning experts on defined projects. Start with one workflow, measure the result, then expand. Prompt engineering for business works best when it is treated as an ongoing process, not a one-off prompt.

 FAQs

What is prompt engineering for business?

Prompt engineering for business is the practice of designing, testing and refining the instructions, context and output formats given to an AI model so it handles a specific business task reliably. Instead of one-off questions, teams use tested prompts and templates that produce consistent, checkable results for tasks such as support replies, summaries, data extraction and content drafting.

How can prompt engineering improve business workflows?

It makes AI output more consistent, easier to review and easier to connect with other tools. Well-designed prompts reduce rework by specifying the audience, rules and format up front. Combined with testing and human review, they help teams use AI for drafting, summarising and extracting information without relying on trial and error each time.

What does a prompt engineer do for a business?

A prompt engineer designs and tests the instructions that guide an AI model. They clarify the business goal, define inputs and outputs, build reusable templates, connect prompts to LLM APIs and company data, evaluate results, and improve reliability. They usually work alongside developers and business teams so the AI fits real processes.

When should a company hire a prompt engineer?

Consider hiring one when AI output needs to be consistent across a team, when prompts must connect to APIs or internal data, or when errors carry compliance or brand risk. A company that uses AI only occasionally for personal drafting, or is satisfied with a ready-made tool, may not need a dedicated specialist.

What skills should a prompt engineer have?

Useful skills include understanding LLM capabilities and limits, prompt design and testing, structured outputs, API knowledge, RAG basics, workflow design and output evaluation. Business-process understanding and clear communication matter just as much, because the aim is a reliable result for a real task, not a clever prompt.

Can prompt engineering automate business processes?

On its own, no. Prompt engineering makes an AI model respond reliably, but automation also needs triggers, tool connections and rules for handling exceptions. Together they can automate parts of a process, such as sorting tickets or drafting replies. Human review is still sensible where mistakes are costly, and AI should not be assumed to replace staff.

What is the difference between prompt engineering and AI automation?

Prompt engineering shapes how an AI model responds to a task. AI automation connects steps, tools and data so a process runs with little manual effort. Automation often uses prompts inside it, so the two are complementary: prompt engineering helps each AI step work reliably, and automation links those steps into a workflow.

14. Sources / References

• Google Search Status Dashboard, Ranking history: https://status.search.google.com/products/rGHU1u87FJnkP6W2GwMi/history
• Google Search Central, “AI features and your website”: https://developers.google.com/search/docs/appearance/ai-features
• Stanford HAI, 2026 AI Index Report, Economy chapter (70% of organisations using generative AI in at least one business function in 2025): https://hai.stanford.edu/ai-index/2026-ai-index-report/economy
• McKinsey & Company, “The state of AI: How organizations are rewiring to capture value” (March 2025): https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-how-organizations-are-rewiring-to-capture-value
• Stack Overflow, 2025 Developer Survey press release (46% not trusting the accuracy of AI output): https://stackoverflow.co/company/press/archive/stack-overflow-2025-developer-survey/
• Anthropic, Prompt engineering overview and “Create strong empirical evaluations”: https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/overview and https://platform.claude.com/docs/en/test-and-evaluate/develop-tests

Modern generative AI development is more than writing prompts. Stack Overflow’s 2025 Developer Survey found 84% of developers use or plan to use AI tools, yet 46% do not trust the accuracy of AI output. That gap is why testing and evaluation sit alongside coding on the skills list.

What Does a Generative AI Developer Do?

A generative AI developer builds software that uses AI models to create text, answers, summaries or actions inside real products. The work varies by project, but common tasks include:

• integrating LLMs into applications and building chatbots or assistants
• connecting models to other software through APIs
• building RAG systems that use company documents
• developing AI agents for multi-step tasks
• working with embeddings and vector databases
• testing AI output and linking AI to business workflows

A customer-support assistant, for example, may need most of these. A simple text-summary feature may need only one or two.

Which Generative AI Developer Skills Matter Most?

A generative AI developer typically needs programming, LLM integration, API development, retrieval, AI application development, evaluation and deployment skills. Not all carry equal weight.

Python, LLM API experience and evaluation matter in almost every project. RAG, embeddings and vector databases matter when the AI must use your own data. AI agents matter when the system must take actions, not just answer. Prompt engineering supports all of these, and security awareness applies to every project that touches business data.

Why Is Python Important for Generative AI Development?

Python is a very common choice for building generative AI applications, largely because the tooling is built around it. Major AI providers publish Python software development kits (SDKs), and frameworks for LLM apps and agents, such as LangChain and LangGraph, offer Python libraries. Python also handles data processing, automation, quick prototyping and backend integration well. In the 2025 Stack Overflow Developer Survey, 54.8% of professional developers (not only AI developers) reported extensive work in Python in the past year.

What LLM Skills Should a Generative AI Developer Have?

A large language model (LLM) is a foundation model trained on large amounts of text to understand and generate language. A developer should understand what these models do well, where they fail, and how to choose between them.

Key LLM skills include knowing how tokens and context windows limit input and cost, designing clear prompts, selecting a model for quality, speed and price, and requesting structured outputs such as JSON that software can read. Developers do not need to train an LLM from scratch to build useful applications. Many projects use existing models through APIs.

What Is RAG and Why Is It Important?

Retrieval-augmented generation (RAG) is a method that retrieves relevant information and gives it to an LLM as context before it answers. The flow is: user question, retrieval, relevant information, model context, generated response. Because the answer draws on retrieved material, RAG can help ground responses in your own data and reduce unsupported answers. It does not remove errors completely.

Embeddings and vector databases make the retrieval step work. An embedding is a list of numbers that represents the meaning of text, so similar passages sit close together. OpenAI’s documentation describes embeddings as measuring how related text strings are. A vector database stores these embeddings and finds the closest matches quickly.

For example, a company can let an AI assistant answer staff questions using HR policies and product manuals. The developer splits the documents into chunks, creates embeddings, stores them, and retrieves the best matches for each question.

What API Skills Does a Generative AI Developer Need?

An API (application programming interface) lets one piece of software talk to another, and it is how most applications reach an AI model. A developer needs to work with model APIs and REST APIs, handle authentication and API keys safely, send and read JSON, manage errors and retries, and respect rate limits and costs. For a business, this is what turns a model into a working feature, such as an assistant inside your CRM or support desk.

What Are AI Agents and What Skills Are Needed to Build Them?

An AI agent is a system that uses a model, tools and a workflow to complete tasks, not just answer questions. For example, it might look up an order, check a policy and draft a reply.

Building one takes tool or function calling, workflow orchestration (deciding what happens in which order), planning for multi-step tasks, managing context or memory, testing agent behaviour, and setting safe permissions. Agents are not always fully autonomous. Many business agents ask a person to approve key steps. Capabilities and designs vary widely, and the Stack Overflow survey found only 31% of developers were using AI agents in 2025.

How Do These Skills Work Together?

A typical flow is: Python → API → LLM → RAG → tools or agents → application → evaluation. Not every project needs every stage.

1. Python holds the application logic together.
2. An API call sends a request to the model.
3. The LLM understands the request and generates text.
4. RAG adds relevant company information when needed.
5. Tools or agents let the system take actions, such as updating a record.
6. The application presents results to the user.
7. Evaluation checks quality, accuracy and safety before and after launch.

A simple summariser may stop at step 3. A support assistant using internal documents needs RAG. A system that acts on orders needs agents too. Knowing which parts a project needs is a skill in itself.

What Should Businesses Look for When Hiring a Generative AI Developer?

Look for evidence of working AI projects, not just a list of tools. Useful signs include Python and LLM API experience, RAG and vector database work, API integration, agent development where your project needs it, and a clear approach to evaluation and security.

Equally important is business understanding. A good developer can explain why a model or architecture suits your problem, what it will cost to run, where it can fail, and how data will be protected. Ask how they would test answer quality, and what they would do if retrieved documents were out of date.

If you need flexible help, WorkFlexi is a marketplace where businesses can connect with generative AI engineers, prompt engineers and wider AI and machine learning professionals for defined projects. Match the person to the skills your project actually needs, rather than hiring for every skill on this list.

 FAQs

What skills does a generative AI developer need?

A generative AI developer needs Python programming, experience with LLM APIs, and knowledge of RAG, embeddings and vector databases. Depending on the project, they also need AI agent and tool-calling skills, prompt engineering, testing and evaluation, deployment, and security awareness. Not every project uses all of these, so the right mix depends on what you are building.

Does a generative AI developer need Python?

Usually, yes. Python is not the only option, but most AI SDKs, LLM frameworks and data tools offer strong Python support. It is widely used for connecting models, processing data and building prototypes. A developer comfortable in another language such as JavaScript can still build AI features, but Python is a common expectation.

What LLM skills should a generative AI developer have?

A generative AI developer should understand what LLMs can and cannot do, including tokens, context windows and hallucinations. They should be able to design prompts, choose a suitable model, call LLM APIs, request structured outputs and evaluate results. They do not usually need to train a model from scratch to build useful business applications.

What is RAG in generative AI development?

RAG, or retrieval-augmented generation, retrieves relevant information from a knowledge source and gives it to an LLM as context before it answers. It can help ground responses in your own documents, which is useful for support assistants and internal search. It can reduce unsupported answers but does not eliminate them, so testing is still needed.

What skills are needed to build AI agents?

Building AI agents takes tool or function calling, workflow orchestration, planning multi-step tasks, API integration and context management. Developers also need to test agent behaviour, set clear permissions and decide where a human should approve actions. Frameworks such as LangGraph can help, but sound design and safety matter more than any single tool.

What does a generative AI developer do?

A generative AI developer builds applications that use AI models to generate text or perform tasks. Typical work includes integrating LLM APIs, building chatbots and assistants, creating RAG systems, developing AI agents, working with embeddings and vector databases, and testing output quality. Responsibilities vary depending on the project and the team’s size.

How do I choose a generative AI developer for my project?

Start with your goal, then look for matching experience: LLM API work, RAG for document-based tools, or agents for multi-step automation. Ask for examples of working projects, how they test accuracy, how they protect data and what the system will cost to run. Prefer clear explanations over long tool lists.

Sources / References

• Stack Overflow, 2025 Developer Survey press release. It reports 84% using or planning to use AI tools, 46% not trusting AI output accuracy, and 31% currently using AI agents: https://stackoverflow.co/company/press/archive/stack-overflow-2025-developer-survey/
• Stack Overflow, 2025 Developer Survey, Technology section (Python at 54.8% among professional developers): https://survey.stackoverflow.co/2025/technology
• OpenAI developer documentation, “Vector embeddings”: https://developers.openai.com/api/docs/guides/embeddings
• Google AI for Developers, Gemini API OpenAI-compatibility page (function calling and embeddings): https://ai.google.dev/gemini-api/docs/openai
• Google Search Central, “Google Search’s Core Updates”: https://developers.google.com/search/docs/appearance/core-updates

You do not need a technical background. Every technical term is explained in plain English.

What Can a Java Developer Build?

A Java developer can build backend systems, web applications, APIs, enterprise software, e-commerce platforms, financial and transaction systems, microservices, cloud-based applications, and internal business tools. Java can also be used for Android apps, although many newer Android apps are written in Kotlin.

Java is a general-purpose programming language. It is object-oriented, which means code is organized into reusable building blocks. It runs on the Java Virtual Machine (JVM), so the same program can run on different operating systems. That is one reason businesses use it for long-lived systems.

Java is not the answer to everything. A simple brochure website rarely needs it. But for software that manages data, users, and business rules, it is a common choice. According to the 2025 Stack Overflow Developer Survey, 29.6% of professional developers had done extensive work in Java in the past year.

What Types of Projects Can a Java Developer Work On?

Here are the main project categories.

Can Java Developers Build Web Applications?

Yes. Java developers can build web applications, such as customer portals, booking systems, and admin dashboards. Java handles the server side: it receives requests, applies business rules, and talks to the database.

Most Java web development uses Spring, a framework (a ready-made toolkit) for common tasks. Spring Boot is a version of Spring that removes much of the setup work. Hibernate helps an application save and read database records without hand-writing every query. In the same survey, 15.6% of professional developers reported using Spring Boot.

Backend Development With Java

Backend development is the part of software users never see. A Java backend developer builds business logic, login and permission systems, data processing, database connections, and server-side applications.

For example, when a customer places an order, the backend checks stock, calculates the price, saves the order, and sends a confirmation. Java works with databases such as MySQL, PostgreSQL, and MongoDB, and developers use Git to track code changes.

Can Java Be Used for Enterprise Software?

Yes. Java is widely used for enterprise software: large, complex systems that support how a company operates. Examples include ERP systems, CRM platforms, banking and insurance applications, logistics platforms, and internal business software.

Java suits these projects because it has been used for large systems for decades, has mature libraries and tools, and has a big pool of experienced developers. Enterprise Java development often focuses on maintainability, since these systems may run for many years.

E-Commerce Platforms

Java developers can build the backend of an online store: product catalogs, customer accounts, order management, payment integrations, and inventory tracking. Shoppers see the storefront, while Java services work behind it to keep data accurate. A developer may build a custom platform or add features to an existing one.

Can Java Developers Build APIs and Integrations?

Java developers can build REST APIs. An API is a set of rules that lets one software system request data from another. A REST API does this over the web. For example, a mobile app uses an API to fetch a customer’s order history from your server.

Java API development also covers integrations, such as connecting a CRM to accounting software or a payment gateway. Spring Boot is a common tool for this work.

What Are Java Microservices?

Microservices split one large application into small, independent services. One service might handle payments, another user accounts, and another notifications. Each can be updated without rebuilding everything.

Java developers commonly build these services with Spring Boot and package them with Docker so they run the same way anywhere, including on cloud platforms such as AWS, Azure, and Google Cloud. Microservices add complexity, so they suit growing systems better than first versions.

Financial and Transaction-Based Applications

Java is often used for applications that process transactions, such as payment systems, billing tools, and account management platforms. These need careful backend logic so records stay consistent. Java’s mature database tools and testing frameworks support that work. Security and performance still depend on how a system is designed and maintained, not on the language alone.

Business Automation and Internal Tools

Java developers can build custom tools that reduce manual work: reporting dashboards, approval workflows, data import tools, and scheduled jobs. Companies often use these tools to connect older systems with newer ones.

How Do These Java Projects Fit Together?

These project types are closely linked. Core Java is the base language. Spring Boot builds backend services on top of it. Those services expose APIs. The APIs read and write data in databases such as PostgreSQL. As a system grows, teams may split it into microservices, which together form an enterprise application or business platform. A skilled Java developer understands this whole chain, not just one piece.

What Skills Should a Java Developer Have?

A strong Java developer knows core Java and object-oriented design first. Beyond that, most project work calls for Spring or Spring Boot, REST API design, SQL and at least one database, Git for version control, and basic Docker and cloud knowledge. Testing and clear communication matter just as much, especially for freelance work where you need to explain progress to non-technical clients.

When Should You Hire a Java Developer?

You should hire a Java developer when your project needs dependable backend logic, API work, database integration, or a system you will maintain for years. If you only need a simple website or a quick prototype, another technology may fit better.

Weigh these factors:

• Existing technology stack: if your systems already use Java or Spring, you need someone who can work in it.
• Backend and API needs: logic, integrations, and data-heavy features.
• Frameworks: Spring Boot, Hibernate, or another specific tool.
• Databases: experience with SQL or NoSQL systems such as PostgreSQL or MongoDB.
• Project duration: a one-time build or ongoing work.
• Maintenance: updating or fixing older Java code.
• Developer experience: junior help for small tasks, senior skill for architecture.

Some businesses choose freelance or flexible Java talent instead of a full-time hire. This is common when they need a specific technical skill, short-term development support, help with an existing project, extra capacity, or a specialist in one Java technology. WorkFlexi is a marketplace that connects businesses with skilled professionals, including freelance Java developers and Spring Boot developers, so you can match the person to the work. For wider server-side needs, you can also look at backend developers.

If your project needs Java skills, explore freelance talent on WorkFlexi and describe what you are building. A clear scope makes it easier to find the right match.

 FAQs

What can a Java developer build?

A Java developer can build backend systems, web applications, REST APIs, enterprise software, e-commerce platforms, financial applications, microservices, and internal business tools. Java can also be used for Android apps, though many newer Android projects use Kotlin. It is best suited to software that handles data, users, and business rules.

What is Java mainly used for?

Java is mainly used for server-side software: backend systems, enterprise applications, web applications, and APIs. It is a general-purpose, object-oriented language that runs on the Java Virtual Machine, so programs can run on different operating systems. Businesses often choose it for systems that must be maintained and updated over many years.

Can a Java developer build web applications?

Yes. Java developers build the server side of web applications, including business logic, user accounts, and database connections. They often use Spring Boot to speed up development and Hibernate to work with databases. Front-end design is usually handled with other tools, so many web projects combine a Java developer with a front-end developer.

Can Java developers build APIs?

Yes. Java developers regularly build REST APIs that let apps, websites, and systems exchange data. Spring Boot is a common framework for this. They can also connect your software to third-party services, such as payment gateways, accounting tools, or CRM systems, so different systems work together without manual data entry.

Is Java suitable for enterprise applications?

Yes. Java has a long history in enterprise software, including ERP, CRM, banking, insurance, and logistics systems. It offers mature frameworks, strong tooling, and a large pool of experienced developers. Whether it is the right fit still depends on your team, existing systems, and project goals.

What projects can a freelance Java developer work on?

A freelance Java developer can take on focused projects such as building a REST API, adding features to a Spring Boot application, connecting two systems, fixing or updating older Java code, or building an internal tool. Freelancers also help with short-term capacity gaps when a team needs extra hands or a specific skill.

When should I hire a Java developer?

Hire one when your project needs backend logic, APIs, database integration, or software you will maintain long term, or when your existing system already runs on Java. Check the required frameworks, project length, and experience level first. For a simple website or short prototype, another technology may be more practical.

Sources / References

Stack Overflow, 2025 Developer Survey, Technology section (most popular technologies). It reports Java at 29.6% and Spring Boot at 15.6% among professional developers: https://survey.stackoverflow.co/2025/technology

What Tools Does a Data Scientist Use?

A data scientist typically uses Python for programming, Pandas for working with tables of data, NumPy for numerical calculations, SQL for getting data out of databases, and Scikit-learn for machine learning. These tools work together rather than compete. Each does a different job.

Not every project needs all five. A reporting task may need only SQL and Pandas. A forecasting project may add Scikit-learn. The U.S. Bureau of Labor Statistics describes the role as collecting, cleaning and analysing data, building and testing models, and presenting findings, and these tools support those steps.

Quick definitions:

• Python: a programming language commonly used across the data science workflow.
• Pandas: a library for working with structured, tabular data.
• NumPy: a library for numerical computing and multidimensional arrays.
• SQL: a language for querying and managing data in relational databases.
• Scikit-learn: a Python library for common machine learning tasks.

Why Is Python Important for Data Science?

Python is a general-purpose programming language that sits at the centre of many data science workflows. It has readable syntax and a large ecosystem of free libraries, and it connects to databases, spreadsheets, and cloud services.

With Python, a data scientist can clean data, run statistical analysis, build machine learning models, create charts with libraries such as Matplotlib, and automate repeated tasks. Pandas, NumPy and Scikit-learn are all Python libraries, so Python is what holds them together. According to the 2025 Stack Overflow Developer Survey, 54.8% of professional developers (not only data scientists) reported extensive work in Python in the past year.

What Is Pandas Used for in Data Science?

Pandas is a Python library for working with structured, table-like data. Its main object is the DataFrame, which looks like a spreadsheet with rows and labelled columns.

Data scientists use Pandas to load files, clean messy values, filter rows, group and summarise data, join tables, and handle missing values. It is also useful for first-look exploration of a dataset.

For example, a retailer may export a year of customer transactions. Pandas can remove duplicate orders, fix inconsistent date formats, flag missing values, and calculate total spend per customer, all before any modelling starts.

What Is NumPy Used for in Data Science?

NumPy is a Python library for fast numerical computing. Its core feature is the N-dimensional array, a grid of numbers that supports quick mathematical operations on whole sets of values at once.

Data scientists use NumPy for calculations such as averages, matrix operations, and random sampling. Pandas and Scikit-learn both rely on it.

The difference in one line: Pandas manages labelled, mixed-type tables, while NumPy handles numerical arrays. For example, customer details with names, dates and amounts suit Pandas, while a large grid of numbers for a calculation suits NumPy.

Why Do Data Scientists Use SQL?

SQL (Structured Query Language) is the standard language for querying and managing data in relational databases. Data scientists use it to pull the right data out of company systems before they analyse it elsewhere.

With SQL, you can filter rows, join tables, and aggregate information, such as summing sales by region. A lot of business data sits in databases, so SQL is often the first step.

For example, a data scientist may use SQL to retrieve a year of customer purchases from a company’s database, then load the result into Python for analysis. In the 2025 Stack Overflow Developer Survey, 61.3% of professional developers reported extensive SQL work, a higher share than for Python, though the survey covers developers generally, not only data scientists. SQL dialects differ between systems, and some data lives outside relational databases.

What Is Scikit-learn Used for?

Scikit-learn is a Python library that provides tools for common machine learning tasks. It covers classification (predicting a category, such as spam or not spam), regression (predicting a number, such as next month’s sales), and clustering (grouping similar items, such as customer segments).

It also includes tools for preprocessing data, selecting models, and evaluating how well a model performs. A typical use is predicting which customers are likely to cancel a subscription.

It has limits. Scikit-learn focuses on classical machine learning and is not the main choice for deep learning, where frameworks such as PyTorch or TensorFlow are more common.

Python vs Pandas vs NumPy vs SQL vs Scikit-learn: What Is the Difference?

They are not alternatives. Python is the language; Pandas, NumPy and Scikit-learn are libraries that extend Python; SQL is a separate language for databases. You cannot simply swap one for another, but the mix changes by project. For example, some SQL-style filtering and grouping can be done in Pandas, and simple analysis may not need Scikit-learn at all.

How Do These Data Scientist Tools Work Together?

A common workflow moves from SQL to Python, through Pandas and NumPy, and into Scikit-learn. Real projects vary, and not every project needs every step.

1. SQL retrieves the data from a database.
2. Pandas cleans and explores the dataset.
3. NumPy supports the numerical operations.
4. Scikit-learn trains and evaluates a suitable model.
5. Python connects each step and automates the process.

The result might be a churn prediction, a sales forecast, or a customer segmentation that a business team can act on. Charts and clear explanations turn the output into decisions.

What Skills Should You Look For When Hiring a Data Scientist?

Demand for the role is growing: the BLS projects U.S. data scientist employment to grow 35% from 2025 to 2035, compared with 3% for all occupations.

Look for solid Python and SQL, a working grasp of statistics, and experience cleaning real data and building machine learning models. Tools alone do not make a good data scientist, though. The BLS also lists analytical, communication, logical-thinking, math and problem-solving skills as important.

In practice, ask whether a candidate can:

• turn a business question into a data question,
• choose a method that suits the problem, not just the newest one,
• explain results to people who are not technical.

Some businesses hire freelance data scientists for a defined project, a first prototype, or extra capacity. WorkFlexi is a marketplace where you can find freelance data scientists and machine learning experts for that kind of work.

If your project needs data science skills, explore freelance talent on WorkFlexi and describe the problem you want to solve. A clear question makes it easier to find the right person.

 FAQs

What tools do data scientists use most?

Data scientists commonly use Python, SQL, Pandas, NumPy and Scikit-learn. Python is the programming language, SQL retrieves data from databases, Pandas cleans and analyses tables, NumPy handles numerical work, and Scikit-learn builds machine learning models. Many also use visualization tools and cloud platforms, and the exact mix depends on the project and the team.

Why is Python used in data science?

Python is popular in data science because it is readable, free, and backed by a large ecosystem of libraries. Data scientists can use it to clean data, analyse it, build machine learning models, create charts and automate tasks in one language. It also connects easily with databases and other tools, which helps workflows run end to end.

What is Pandas used for?

Pandas is a Python library for working with structured, table-like data. Data scientists use it to load files, clean messy values, handle missing data, filter and group rows, join tables and explore a dataset before analysis or modelling. Its main data structure is the DataFrame, which resembles a spreadsheet with labelled columns.

What is NumPy used for in data science?

NumPy is a Python library for numerical computing. Its N-dimensional array lets data scientists run fast mathematical operations on large sets of numbers, such as averages, matrix calculations and random sampling. Other libraries, including Pandas and Scikit-learn, rely on it, so it often works behind the scenes rather than being the tool people talk about most.

Why do data scientists need SQL?

Data scientists need SQL because much business data is stored in relational databases. SQL lets them filter records, join tables and summarise information so they pull exactly the data they need for analysis. Without it, they depend on others to export data for them, which can slow projects down.

What is Scikit-learn used for?

Scikit-learn is a Python library for classical machine learning. It offers tools for classification, regression, clustering, data preprocessing, model selection and model evaluation. Data scientists use it to build and test models such as customer churn predictors. It is not designed for deep learning, where frameworks like PyTorch or TensorFlow are more common.

Do data scientists need to know Python, SQL and machine learning?

Many data science roles expect solid Python and SQL, plus a working knowledge of statistics and machine learning. The depth varies: an analytics-focused role may lean on SQL and Pandas, while a modelling role goes deeper into machine learning. Communication and business understanding matter too, because results only help if others can act on them.

Sources / References

• U.S. Bureau of Labor Statistics, Occupational Outlook Handbook, Data Scientists (last modified Aug 27, 2026). It gives 35% projected growth for 2025–35 against 3% for all occupations, and the duties and important qualities cited: https://www.bls.gov/ooh/math/data-scientists.htm
• Stack Overflow, 2025 Developer Survey, Technology section. It reports Python at 54.8% and SQL at 61.3% among professional developers: https://survey.stackoverflow.co/2025/technology
• pandas and NumPy project descriptions (official repositories): https://github.com/pandas-dev/pandas and https://github.com/numpy/numpy