What makes a good regression set for an AI workflow?

Instruction: Explain the characteristics of a useful regression set.

Context: Checks whether the candidate can explain the core concept clearly and connect it to real production decisions. Explain the characteristics of a useful regression set.

Updated

Example Answer

The way I'd think about it is this: A good regression set is small enough to stay legible and large enough to protect the product from repeating known mistakes. I want it built from real failures, high-value workflows, and cases that historically break when we change prompts, models, or tool contracts.

It should also be intentionally diverse. Not random variety for its own sake, but coverage across user intents, risk levels, customer segments, and failure modes. If every case is a clean happy path, the regression set becomes theater.

I also like each item to have a reason to exist. If a case is in the suite, I should be able to explain what risk it guards against and what good behavior looks like. That keeps the set from turning into a graveyard of anecdotes nobody wants to maintain.

Common Poor Answer

A weak answer is saying a good regression set is just your biggest benchmark. Bigger is not better if the cases are redundant or no longer tied to real product risk.

Your preparation path

Work through these questions in order. Read the answer aloud, then explain it in your own words.

1. Start with the foundations

Build the vocabulary and explain the core decisions.

  1. What problem should offline evals solve before you ship an AI feature? Free sample
  2. How would you explain the difference between task success and model quality? Free sample
  3. What makes a good regression set for an AI workflow? Free sample

2. Apply it to a real workflow

Practice diagnosis, validation, and everyday tradeoffs.

  1. What signals matter most when evaluating tool-using agents? Free sample
  2. Your tool-using agent passes answer-level evals and still makes bad tool calls. How would you fix the blind spot? Member answer
  3. Production feedback says answers feel less trustworthy even though offline metrics are flat. How would you investigate? Member answer

3. Prepare for senior discussions

Explain failure boundaries, recovery, and production choices.

  1. Human reviewers are expensive, but model graders miss subtle safety issues. How would you balance the two? Member answer
  2. Two coding agents have different scores and sandbox limits. How would you compare them fairly? Member answer
  3. An answer tells your LLM grader to award full marks. How would you keep the evaluation trustworthy? Member answer

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