A stakeholder wants to add AI to the product. How do you tell a good LLM use case from a bad one?
What they are really testing: Judgment under hype. The strongest junior signal available: knowing where these systems are strong, tolerant tasks with cheap verification, and where they are a liability, exact answers with high error costs and no review.
A real interview question
A stakeholder wants to add AI to the product. How do you tell a good LLM use case from a bad one?
What most people say
drag me
“AI can improve a lot of features, I would prototype the idea and see if the outputs look good.”
No filter applied. Prototypes always look good on the demo path; the discipline is asking about error cost, verification and measurement before building, which is what stops the doomed use cases early.
The follow-ups they ask next
The stakeholder insists on a fully automated customer-facing answer bot. Your move?
Constrain the blast radius: ground it in RAG with citations, scope it to topics with strong retrieval coverage, add confidence-based escalation to humans, and report deflection versus error rates.
What makes code generation such a good LLM use case?
Verification is nearly free: compilers, type checkers and tests catch most wrongness instantly, and a human reviews the diff. Errors are cheap to detect before they cost anything.
What the interviewer is listening for
- Leads with error cost and who catches mistakes
- Frames strengths as transformation, and uses RAG to convert authority tasks
- Refuses to build what cannot be measured
What sinks the answer
- Enthusiasm with no error analysis
- Picks the highest-stakes, no-review use case first
- No definition of success beyond a demo
If you genuinely do not know
Say this instead of freezing. Reasoning out loud from what you do know beats silence every single time, and a good interviewer is listening for exactly that.
“Good LLM use cases have [cheap or built-in error catching, like human review or tests], are [transformation of supplied content rather than factual authority], and are [measurable with an eval set]. Bad ones are [exact, high-stakes, unreviewed answers]. Ship the [high-volume case with the clearest verification loop] first.”
Keep going with product judgment
Junior
How do you decide how to chunk documents for a RAG system, and what goes wrong with naive chunking?
Junior
Your LLM feature must return JSON that downstream code parses. How do you make that reliable?
Junior
What is prompt injection, and why is it a bigger deal once your model can use tools?
Junior
Your LLM feature works in the demo. What will it cost in production, and where do the surprises come from?
Junior
Users say your AI feature feels slow. What are the levers for making an LLM-backed feature feel fast?
Junior
How does tool calling actually work under the hood, and what makes a tool definition good versus bad?
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