How to test LLM features: writing automated evals and running them in CI

▤ Summary

This article explains how to build automated evaluations for LLM features and run them in CI. It shows how to define evaluation cases, load JSONL test data, and implement checks such as non-empty output, no secrets, expected-content matching, and groundedness. It also covers LLM-as-judge scoring, pairwise comparisons, pass-rate tracking, and regression gates that block PRs when quality drops beyond a threshold. The piece positions eval-driven development as a repeatable engineering workflow for teams shipping AI products.

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