Dataford · GenAI Report
The Generative AI & LLM Interview Report 2026
Two years ago these questions barely existed. Now generative AI and LLMs are the fastest-growing — and second-hardest — corner of the data interview. We broke down 517 of them to show what gets asked, how hard it is, and where it is concentrated.
Summary · Key findings
The newest topic in the data interview is already one of the hardest. 30% of generative-AI and LLM questions are rated hard — second only to statistics. A subject that barely existed two years ago now sets a steep bar.
This is the home turf of the AI Engineer. The role has climbed to #2 in prep demand, and these questions are why. Data Scientists and AI Engineers carry the topic; it is the clearest signal of where the new jobs are.
The questions concentrate at the AI labs. OpenAI asks the most generative-AI questions of any company by a wide margin, with Google and Anthropic next. If you are interviewing at a lab, this is not a side topic — it is the main one.
It is not just prompt engineering. The topic spans applied LLM work — retrieval, vector search, evaluation, and serving — on top of classic NLP foundations. The shallow framing of “just write good prompts” misses most of it.
The hard questions are about judging and shipping models. Expect to defend how you would evaluate an LLM system or deploy one reliably — not just call an API. That is where the difficulty, and the differentiation, lives.
Most interview topics evolve over decades. This one appeared in about eighteen months — and it arrived hard. Generative AI is the rare subject that went from absent to central without a gentle phase in between.
We looked at how it actually shows up in interviews now: how difficult the questions are, which roles face them, and where they concentrate. The pattern is a useful map of where the field is moving faster than the job titles can keep up.
If you only take one thing from this report, take the framing: this is engineering around models, not conversation with them. The full method is at the end.
The bar
New, and already brutal
A young topic might be expected to ask gentle questions while the field finds its feet. The opposite is true here: generative AI is the second-hardest subject in the entire data interview.
The difficulty comes from the gap between using a model and reasoning about one. Anyone can call an API; far fewer can defend how they would evaluate an LLM system, control its failure modes, or serve it reliably. That gap is exactly what the hard questions are designed to expose.
The role
The AI Engineer's home turf
New topics show up in interview data before they settle into job titles. This one has already found its role: AI Engineer, the title that has climbed to second in prep demand across the platform.
Data Scientists still field the most of these questions in raw terms, but the AI Engineer is the role being built around them. Analysts barely appear — a reminder that this is, for now, a specialist track, and the clearest bridge from classic data work into the fastest-growing part of the market.
Where
It lives at the labs
Generative AI is not evenly spread across employers. It clusters where the models are built, and one company sits far out in front.
OpenAI asks more generative-AI questions than any other company, ahead of Google and Anthropic, with enterprise players like Intuit and Salesforce following as they build AI into their products. At a lab, this is not one round among many — it is the interview.
Outlook
How to prepare for the GenAI round
Prepare for the engineering, not the chat. The questions that decide these loops are about retrieval, evaluation, and deployment — how you would build a reliable system around a model, and how you would know it is working.
For data professionals eyeing the AI Engineer path, this is the single highest-leverage topic to invest in: it is hard, it is concentrated at the most sought-after employers, and it sits on the role with the most momentum in the field.
Practice the GenAI questions labs actually ask
Real LLM and generative-AI questions, from evaluating an LLM system to vector search in RAG.
Practice GenAI questionsFAQ
Frequently asked questions
What do generative AI interviews test?+
Applied LLM work on top of NLP foundations: retrieval and vector search, prompting, evaluating LLM systems, and deploying them reliably. It goes well beyond writing prompts — the hard questions are about judging and shipping models.
How hard are LLM interview questions?+
Among the hardest of any topic. 30% of generative-AI and LLM questions are rated hard — second only to statistics — with 23% easy and 46% medium. It is a young subject with a steep bar.
Which companies ask the most GenAI questions?+
The AI labs lead. OpenAI asks the most by a wide margin, followed by Google and Anthropic. At a lab, generative AI is the core of the interview rather than a side topic.
Is generative AI interviewing just prompt engineering?+
No. Prompting is a small part. The topic spans retrieval-augmented generation, vector search, model evaluation, and serving — the engineering around models, not just talking to them.
Which role should I target for AI work?+
AI Engineer is the role built around this topic and has climbed to #2 in prep demand. Data Scientists also face a lot of generative-AI questions, but AI Engineering is the title most directly aligned with the new work.
Methodology
How this report was built
This report draws on 517 published questions across Dataford's Generative AI & LLMs and NLP categories, each tagged with a difficulty, the roles it applies to, and any associated companies.
Role and company figures use those tags; a question can apply to several roles, and company coverage is uneven, so company counts indicate emphasis rather than a precise ranking. The AI Engineer demand rank references Dataford's prep-demand signal across the platform.
The bank reflects the generative-AI questions companies ask for these roles as captured and structured by Dataford. Figures are current as of June 2026.