Mid-level interview questionAsked at Luma AI · interview write-up
Build a system to manage prompts and model behaviour in production, then defend its versioning and rollout trade-offs.
Model answer
Treat prompts as versioned config: a registry stores each prompt's template, model, parameters and output schema as an immutable version that the app references by ID. Every change runs a regression suite of golden cases and judge checks before promotion.
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Anything that can change the output counts as a change, not only edits to the prompt text.
Text version
- Anything that can change the output counts as a change, not only edits to the prompt text.
- Run the whole fixed suite automatically, the way unit tests run on code.
- Gate on broken cases, not only on the total: a better average can still hide a broken case you care about.
- When something slips through to users, add it as a case so it can't silently return.
Roll out like a feature flag, with instant rollback. Log the prompt version and model with every output, so production quality can be sliced by version.
Practise the rest
10 Regression tests questions, 492 in all, each with a model answer. Free.
More from Luma AI interview questions.
More Regression tests interview questions
- Why version prompts, and what should be logged with every output?
- A task returns JSON with a label. Which scorer fits it, and why not exact match or a contains check?
- Prompt B passes 9 of 12 test cases; prompt A passed 8. Do you ship B?
- On a 20-case suite, a new prompt scores 5 points higher. What can you honestly report?
- Design the regression gate for a production prompt. What triggers a run, what blocks a merge, and what about the model changing?
- How do you build a golden dataset, and use it for regression testing?