How to Build a Reliable Evaluation Harness for LLM Apps in Python

A structured approach to evaluating large language model applications in Python requires more than standard unit tests, since LLM outputs are probabilistic prose rather than fixed values. The core of this evaluation framework consists of three components: a curated golden dataset of representative input-output pairs, automated scoring methods, and regression testing tied to the build pipeline. Golden datasets should be small enough to run in minutes and must cover common cases, known failure points, adversarial inputs, and scenarios where the model should appropriately hedge or refuse. Each test case is designed to be scored by one of three methods — exact match, keyword containment, or rubric-based LLM judging — depending on the nature of the expected output. This approach complements retrieval metrics like recall@k and MRR by separately measuring whether generated answers built from retrieved chunks are actually accurate and useful.