The GenAI interview goes deeper than the demo.
LLMs, RAG and fine-tuning, in a live interview that pushes past the surface answer.
Sample scorecard
Generative AI Engineer
What the report tells you to close:
Expect follow-ups on retrieval quality, evaluation, and how you handle hallucination.
The short version
Why the Genai Engineer interview is hard
Generative AI interviews test judgement about when to use which tool. Prompting, retrieval and fine-tuning solve different problems, and a large part of the assessment is whether you know which one a given failure actually calls for.
What to expect
The rounds you should rehearse
RAG design
chunking, retrieval quality and why the wrong context produces the wrong answer.
Fine-tuning versus retrieval versus prompting
choosing based on the failure you are seeing.
Hallucination
reducing it, detecting it, and designing for the times it happens anyway.
Avoid these
The mistakes that quietly sink candidates
Proposing fine-tuning for a problem that is plainly a retrieval failure.
Treating chunking as a detail when it frequently determines whether RAG works at all.
Having no evaluation approach, so no way to tell whether a change helped.
Reading the questions is not practicing them.
Run a live voice mock tuned to the Genai Engineer interview. It follows your answers, probes the gaps, and scores you like a senior interviewer would.
FAQ
Questions, answered.
When should I fine-tune instead of using RAG?▾
How do I talk about hallucination well?▾
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