HowtoEvaluate&TestLLMApplications

LLM outputs are non-deterministic, subjective, and context-dependent. Traditional software testing (assert expected === actual) doesn't work. You need a different evaluation framework, one that embraces ambiguity while still catching regressions.

The biggest mistake teams make is shipping LLM features without any evaluation framework. The second biggest is over-investing in complex metrics before establishing basic quality baselines.

LangSmith, Braintrust, and Promptfoo are purpose-built for LLM evaluation. For simpler setups, a spreadsheet of test cases with a Python script running evaluations is often enough to start.

The key insight: evaluation is not a one-time activity. It's a continuous process that runs on every change. Build it into your CI/CD pipeline from the beginning, since it's much harder to add retroactively.