Explain How Evals Measure the Quality of LLM-Powered Product Features

Explain what evals are for an AI product built on large language models, and how they work in practice. ```hint Start from one feature Pick a single AI feature and ask what a good output looks like for it, which examples you would test it on, and who or what would decide whether each output passes. ``` ### Clarifying Questions - Does "evals" here mean offline test suites run before a change ships, measurement of live traffic, or both? - Is the system a single model call, an assistant that answers from retrieved documents, or an agent that takes actions with tools? - Should the explanation include how a product manager uses evals to decide whether a feature is ready to launch? ### What a Strong Answer Covers - The parts of an eval: test inputs, a definition of a good output, a grading method and a score that can be compared across versions. - The main ways to grade outputs, such as code checks, human review and model-based judges, and when each can be trusted. - Where evals sit in the product lifecycle: during development, as a release gate on every change, and when monitoring production. - How the test set is built and kept current, including from failures seen in production. - The limits of evals: biased judges, overfitting to the test set, small samples and scores that miss what users care about. ### Follow-up Questions - After a prompt change, your model-graded score goes up but user complaints also rise. What could explain it, and what would you check first? - How would your evals differ for an assistant whose wrong answers could cost users money, such as one answering tax or bookkeeping questions? - How would you decide how many test cases you need before trusting a difference between two versions?