Top Questions to Ask Before Hiring a Generative AI Expert

Before hiring a generative AI expert, ask about their past project experience, which LLMs and technologies they’ve worked with, how they’d approach your specific business problem, and their experience with RAG (Retrieval-Augmented Generation) and AI agents. Also ask how they evaluate model performance, reduce hallucinations, handle data privacy and security, and measure project success. Meaningful experience means having actually deployed AI solutions into production,  not just experimented with AI tools. When evaluating past work, look beyond the number of projects to portfolio quality, business outcomes, the reasoning behind technical decisions, and whether the work reached real users. A generative AI expert differs from an AI developer or ML expert mainly in focus, strategy and model evaluation versus coding and integration or predictive modeling, though titles and responsibilities vary between companies.

Ask about their past generative AI projects, which LLMs and tools they’ve used, how they’d approach your business problem, their experience with RAG and AI agents, how they evaluate model performance and reduce hallucinations, how they handle security and data privacy, and how they’d measure project success.

Two candidates can both call themselves a generative AI expert while having very different levels of real experience. One might have built and deployed a production RAG application handling sensitive business data; the other might have experimented with a few prompts in a chatbot interface. Without the right questions, it’s difficult to tell the difference and that gap is exactly where hiring mistakes happen.

This article walks through the questions that actually reveal whether someone can deliver a working, secure, business-relevant AI solution.

What Should You Ask Before Hiring a Generative AI Expert?

At minimum, ask about their past project experience, which LLMs and tools they’ve worked with, how they’d approach your specific problem, their experience with RAG and AI agents, how they evaluate model performance, how they handle hallucinations and security, and how they define project success. Below are the ten questions worth asking in more depth.

1. What generative AI projects have you worked on? Look for specifics, what was built, for whom, and what happened after launch.
2. Which LLMs and AI technologies do you have experience with? Different models suit different tasks; broad exposure suggests adaptability.
3. Can you explain how you’d approach my business problem? This reveals whether they think in terms of your outcome, not just the technology.
4. Have you built RAG-based applications? Retrieval-Augmented Generation (RAG) lets an AI application pull relevant information from a trusted knowledge source before generating a response, a common requirement for business use cases involving internal data.
5. How do you evaluate AI model performance? A credible expert should have a defined method, not just intuition.
6. How do you reduce hallucinations? Hallucinations are confident but incorrect AI outputs; understanding how to minimize them matters for anything customer-facing.
7. How do you handle AI security and data privacy? Especially important if the application touches sensitive or regulated data.
8. How will you measure the success of the project? Clear metrics prevent vague, open-ended engagements.
9. How do you approach deployment and scaling? Many AI projects stall between prototype and production, this question surfaces that risk early.
10. Can you share relevant examples of previous work? Portfolio evidence, even under NDA-appropriate summaries, is more convincing than a list of skills.

What Experience Should a Generative AI Expert Have?

Meaningful experience usually includes hands-on work with generative AI applications, LLM integration, APIs, RAG pipelines, vector databases (which store information for fast, meaning-based retrieval), prompt engineering, and depending on the project  AI agents and fine-tuning. Cloud deployment and security awareness matter too.

There’s a real difference between someone who has experimented with AI tools and someone who has built or deployed a working solution. Experimentation might mean testing a chatbot or generating content with an off-the-shelf tool. Deployment experience means the person has taken a model through integration, testing, security review, and monitoring in a live system — a much higher bar, and the one that matters for business-critical work.

What Technical Questions Should You Ask a Generative AI Expert?

You don’t need to become technical yourself, you need answers you can understand.

What LLMs have you worked with? Experience across multiple models (rather than just one) often indicates they can choose the right tool for a given task instead of forcing every problem into one approach.

Have you built RAG-based applications? Ask for a concrete example, such as a knowledge assistant retrieving information from internal documents.

How do you reduce AI hallucinations? Reasonable answers include grounding responses in verified data, structured evaluation, and human review steps — not just “we prompt carefully.”

How do you evaluate an AI application? Look for mention of defined testing processes, not just “it worked in our demo.”

How do you handle data privacy and AI security? This should include how sensitive data is stored, accessed, and protected  not a vague assurance.

How Can You Evaluate a Generative AI Expert’s Previous Work?

The number of past projects alone tells you little. Instead, look at:

• Portfolio quality, depth of documentation, not just a project list
• Relevant project experience, similar industry or problem type
• Business outcomes, did the AI solution actually solve the intended problem?
• Technical decision-making, can they explain why they chose a particular approach?
• Production experience, did the project reach real users, or stay a prototype?
• Communication skills, can they explain technical trade-offs in plain language?

A candidate who can walk through why they made specific decisions is usually more valuable than one who simply lists technologies they’ve touched.

How Do You Know If a Generative AI Expert Understands Your Business Problem?

Technical skill without business understanding often leads to solutions that work in isolation but don’t fit how your company actually operates. Ask: How would you approach this problem? What information would you need before starting? Which approach would you recommend, and why? What risks do you see? How would you measure success?

A strong expert should be able to translate your business requirement into a practical AI approach ,and should be comfortable saying “I’d need to understand X before recommending an approach” rather than jumping straight to a solution.

If your team is going through this evaluation process and wants to shortcut the search, Workflexi connects businesses with vetted generative AI, LLM, and AI development professionals who can be evaluated against exactly this kind of checklist.

FAQs

What is a generative AI expert?

A generative AI expert is a professional with hands-on experience building, integrating, and deploying generative AI applications, including LLMs, RAG systems, and AI agents, to solve real business problems, not just someone who has used AI tools casually.

What skills should a generative AI expert have?

Core skills include LLM integration, RAG implementation, API development, model evaluation, security awareness, and cloud deployment, paired with the ability to translate business requirements into practical AI solutions.

How do I hire a generative AI expert?

Start by reviewing their project portfolio for production (not just prototype) experience, ask targeted technical and business questions, and evaluate their communication skills and understanding of your specific problem before making a decision.

What questions should I ask a generative AI expert before hiring?

Ask about past projects, LLM experience, their approach to your specific problem, RAG and evaluation methods, hallucination reduction, security practices, and how they’d measure project success.

How do I evaluate a generative AI expert’s experience?

Look beyond the number of projects assess portfolio depth, business outcomes achieved, the reasoning behind technical decisions, and whether their work reached production or stayed at the prototype stage.

What is the difference between a generative AI expert and an AI developer?

The roles overlap and titles vary by company, but generative AI experts often focus more on strategy, model selection, and evaluation, while AI developers focus more on coding, integration, and deployment.

When should a business hire a generative AI expert?

Consider hiring one when you have a specific use case (like a knowledge assistant or automation tool), existing AI experiments that haven’t reached production, or a need for specialized skills your internal team doesn’t have.