AI Engineering · LLM applications that ship
Why are evaluations mandatory for LLM features?
Short answer
LLM output varies and prompts change behaviour unpredictably. Evals turn quality into something you can measure before and after a change instead of discovering regressions in production.
Traditional tests assert exact outputs. LLM features produce a distribution of plausible answers, so exact assertions either fail constantly or test nothing useful. Evaluations define representative inputs and a way to score outputs, giving the team a regression signal when a prompt, model or retrieval change ships.
- They make trade-offs explicit: a prompt that fixes one case often breaks another.
- They let you compare models and prompt versions on the same inputs.
- They catch silent failures such as lost citations or format drift.
- They give product and engineering a shared definition of quality.
Follow-ups you should expect
Interviewers use these to check whether the first answer was understanding or memorisation.
- 1What would you do if the team has no eval data yet?
- 2How many examples are enough to start?
- 3Who should own the eval suite?
Say it in the interview
LLM features do not fail like deterministic code, so normal unit tests are not enough. Evals give me a repeatable way to score quality on representative inputs and compare before and after a change. Without them, every prompt edit is a guess and regressions show up as user complaints. I start small with real queries and known failure cases, then grow the set as the product matures.
Use this as a model, not a script. Saying it in your own words is what makes it survive follow-up pressure.
Related questions
Reading is step one
Now say it out loud.
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