Evaluating LLM Apps in Python

Introduction Building Reliable LLM Applications in Python put it plainly: treat model output as a hypothesis to verify, not a fact to trust. Testing Best Practices in Python put the same discipline in pytest terms: a suite only earns trust by asserting the right things at the right level, unhappy paths included. This post is where those two ideas meet — a pytest assertion either passes or fails against a fixed expected value; an LLM's output is a paragraph of prose that might be right in spirit while differing token-for-token from anything you wrote down in advance. Evaluating it takes a harness, not an assert . That harness has three parts: a golden dataset of representative cases with known-good expected behavior, scoring that turns each case into a pass/fail or a number, and regression testing that runs the harness on every change and fails the build when the score drops. Making RAG Accurate in Python already gave you half of this story — recall@k, precision@k, MRR, nDCG measure whether retrieval found the right chunks. This post measures the other half: whether the generated answer built from those chunks is actually good, which is a genuinely different question a retrieval metric can't answer on its own. Everything below is illustrative, non-executed Python, grounded in the same Anthropic SDK shapes as posts 10/11. The Golden Dataset: Curating Cases, Not Just Inputs A golden dataset is a small, hand-curated set of (input, expected behavior) pairs that represents the ways your application is actually used — not a random sample, and not just the cases that already work. Each case needs enough structure to be scored automatically later: from dataclasses import dataclass , field @dataclass class EvalCase : id : str category : str # "extraction", "qa", "summarization", ... input : str # the prompt/question sent to the system under test expected_exact : str | None = None # non-None only for cases scorable by exact match must_contain : list [ str ] = field ( default_f