CrackML by @ml.with.umang
Interview questions / GenAI & LLM
GenAI & LLM interview question

How would you evaluate generative AI quality for a customer-facing product?

How would you evaluate generative AI quality for a customer-facing product?

mediumconceptEvidence 40/1001 source reportAmazon

The 60-second answer

Separate task quality, factuality/grounding, safety, latency, and cost instead of one aggregate score. Use representative held-out prompts plus deterministic checks where possible.

Build the answer in this order

1
Define the mechanism

Separate task quality, factuality/grounding, safety, latency, and cost instead of one aggregate score.

2
Explain the architecture

Use representative held-out prompts plus deterministic checks where possible.

3
Compare trade-offs

Calibrate human review or LLM-as-judge against reference examples and periodic audits.

4
Close with serving + evaluation

Track slice-level regressions and uncertainty across model or prompt changes.

A useful interview mental model

This is the shape of a strong answer—not a script to memorize.

01Definition
02Mechanism
03Trade-offs
04Failure modes
05When to use

Senior-level signal

  • Design evals around real failure modes and adversarial cases, not benchmark averages alone.
  • Prevent evaluation contamination and version datasets so improvements remain trustworthy.

What the interviewer is really testing

Understanding beyond prompting: architecture, retrieval, evaluation, inference, safety, latency, cost, and failure recovery.

Likely follow-up questions

What assumption makes this approach work?
When would you choose the strongest alternative instead?
What production or data failure mode changes your answer?

Common weak-answer patterns

  • Reciting a definition without mechanism or assumptions.
  • Claiming one technique is always better without a data regime.
  • Stopping before failure modes, validation, or deployment implications.