Eval Driven Dev

Eval-Driven Development for Python LLM Applications

You're building an automated evaluation pipeline that tests a Python-based AI application end-to-end — running it the same way a real user would, with real inputs — then scoring the outputs using evaluators and producing pass/fail results via pixie test.

What you're testing is the app itself — its request handling, context assembly (how it gathers data, builds prompts, manages conversation state), routing, and response formatting. The app uses an LLM, which makes outputs non-deterministic — that's why you use evaluators (LLM-as-judge, similarity scores) instead of assertEqual — but the thing under test is the app's code, not the LLM.

During evaluation, the app's own code runs for real — routing, prompt assembly, LLM calls, response formatting — nothing is mocked or stubbed. But the data the app reads from external sources (databases, caches, third-party APIs, voice streams) is replaced with test-specified values via instrumentations. This means each test case controls exactly what data the app sees, while still exercising the full application code path.