AI & Technical question
A customer updates an LLM-based agent and wants confidence that quality improved before release. How would you design the simulation and evaluation system: test-case generation, coverage of critical scenarios, pass/fail criteria for non-deterministic outputs, regression detection, and the release gate for risky changes?
- Sierra
- AI & Technical
- Hard
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What this question tests
Ability to design a rigorous pre release evaluation system for a non deterministic AI agent, including how to define pass and fail without exact match outputs.
How to approach it
- Design test case generation from two sources: real historical conversations covering common scenarios, and synthetic edge cases targeting known failure categories, so coverage is not limited to what has already happened.
- Define coverage explicitly by scenario category, common request types, edge cases, and adversarial or policy sensitive conversations, tracking what percentage of each category the test suite covers.
- Define pass and fail criteria for non deterministic outputs using rubric based grading against key behaviors, for example did the agent correctly resolve the issue, follow policy, and avoid an unsafe response, rather than exact text matching.
- Detect regressions by running the full suite against both the current and updated agent version and flagging any scenario where the grade drops, not just an aggregate score change.
- Set the release gate as a combination of an aggregate pass rate threshold and zero tolerance for regressions in a defined critical scenario category, like safety sensitive conversations.
- Build in human spot checking of a sample of grades to keep the automated grading itself honest over time.
What a strong answer includes
- Uses rubric based grading for non deterministic outputs instead of exact match, since customer support responses are rarely identical.
- Tracks regression at the individual scenario level, not just an aggregate score, so a hidden regression cannot hide behind an average improvement.
- Sets a stricter gate for critical scenario categories, like safety, than for general quality.
- Includes human spot checking of the automated grader itself, showing awareness that graders can drift or be gamed.
Common mistakes
- Relying on a single aggregate quality score that could mask a regression in a critical scenario category.
- Using exact match or overly rigid pass criteria that do not fit non deterministic language output.
Likely follow-up questions
- How would you keep the test case suite from going stale as the agent and customer needs evolve?
- What would you do if the automated grader and a human reviewer disagreed on a borderline case?
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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