AI & Technical question
What evaluation metrics can be used to judge LLM generation quality in your AI Products?
- AI & Technical
- Hard
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What this question tests
Tests technical fluency in AI product evaluation: can you name concrete, appropriate metrics for judging generative quality rather than reusing generic software metrics.
How to approach it
- Clarify the use case: evaluation metrics differ for a chatbot, a summarization tool, or a code-generation feature, so name the assumed use case, for example a customer-support chatbot.
- Split metrics into automated and human-judged categories, since generative quality is not fully captured by automated scores alone.
- Name automated metrics: relevance and factual-consistency scoring (often via a separate LLM-as-judge or retrieval-grounding check), response latency, and format/structure compliance for structured outputs.
- Name human-judged metrics: helpfulness and correctness ratings from human reviewers on a sample of outputs, and user-facing signals like thumbs-up/down feedback or follow-up question rate (indicating the first answer was insufficient).
- Address safety and reliability: hallucination rate (factual claims not grounded in source data) and refusal-rate calibration (not refusing reasonable requests, not answering unsafe ones).
- Tie it together: use automated metrics for fast, cheap iteration during development, and human evaluation plus live user feedback as the ultimate quality bar before and after launch.
What a strong answer includes
- Names LLM-as-judge and human evaluation explicitly as complementary to purely automated metrics, showing awareness of how generative quality is actually assessed in practice.
- Includes hallucination rate as a distinct, named metric, which is the central quality risk specific to generative AI unlike traditional software.
- Uses a real behavioral proxy, follow-up question rate, as a signal that the first response failed to satisfy the user, which is a practical production signal.
- Separates fast automated iteration metrics from slower, higher-fidelity human evaluation, showing a realistic evaluation pipeline, not just a list of scores.
Common mistakes
- Listing only standard ML metrics like accuracy or F1 score, which do not directly apply to open-ended generation quality.
- No mention of hallucination or factual grounding, the central risk in generative quality evaluation.
- Treating evaluation as a one-time pre-launch check instead of an ongoing production signal.
Likely follow-up questions
- How would you build a labeled dataset for human evaluation at scale?
- How would you detect hallucinations automatically without human review on every response?
- How would these metrics change for a creative-writing feature versus a factual Q&A feature?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture