2 posts tagged with "testing"

A guard test is proven only once you have watched it fail on a defect you planted on purpose — and the plant stays, so it fails again on every run. Every guard worth keeping asserts an absence: no node writes, every model node is guarded, every declared file ships. Absence has a pass path that has nothing to do with your code. The scan looked in the wrong directory, the pattern never matched, the node list it walked was empty.

Your RAG pipeline passes all 20 of your hand-written test questions. It retrieves the right context, generates grounded answers, and the demo looks great. Then it goes to production, and users start asking the 21st question — the one that exposes a retrieval gap, a hallucinated citation, or a context window that silently truncated the most relevant chunk. You had 20 tests for a knowledge base with 55 documents. That's 0.4% coverage. The other 99.6% was untested surface area.

This guide shows how to close that gap. We walk through a production implementation that generates 330+ synthetic test cases from 55 AI engineering lessons, evaluates a LangGraph-based RAG pipeline across 10+ metrics, and runs hyperparameter sweeps to find optimal retrieval configurations — all automated with DeepEval and pytest.