AI EngineeringInterview preparation
Intermediate·Concept·2 min read

Evaluation

Practice this question

Beginner — Why isn't normal unit testing enough?

  • Model outputs vary and can be plausible but wrong.
  • I still unit-test deterministic code, but also evaluate representative tasks, support from evidence, tool behavior, and safe failure using versioned datasets and rubrics.

Intermediate — Faithfulness versus correctness?

  • Faithfulness asks whether the answer follows its evidence.
  • Correctness asks whether it is the right answer for the applicable task and facts.
  • An answer can faithfully repeat an outdated or inapplicable policy and remain wrong.

Senior — How do you trust an LLM judge?

  • I calibrate it against human-labeled cases, test adversarial outputs, track disagreement, and version its rubric/model.
  • I combine it with deterministic checks and never make it the sole permission or financial gate.

Scenario — A new prompt raises average accuracy but causes one tenant leak. Ship?

  • No.
  • Severe boundary violations are release blockers regardless of the average.
  • I investigate the cause, fix the enforced access boundary, add the case to the security suite, and rerun relevant regression and quality checks.
Adapted from 13_AI.MD · “29.15 Evaluation”. Code examples are study fragments. Difficulty and core labels are editorial estimates; version-specific behavior should be checked against your project.