Evaluating LLM Apps in Python

Short summary

Build reliable LLM applications with a three-part evaluation framework: golden datasets of carefully curated test cases representing real usage patterns, deterministic scoring for objective criteria (exact match, fact verification) supplemented by LLM-as-judge for subjective quality, and regression testing after each change. This applies unit-testing rigor to probabilistic outputs.

• •Golden datasets should be small, curated sets of representative real-world cases, not random samples
• •Use deterministic scoring (exact match, fact containment) before calling an LLM judge to evaluate quality
• •Run regression testing after every change to catch quality drops and prevent regressions

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