
The Eval Tool I Adopted but Won't Rely On
I migrated my eval harness to a managed tool, got it working, and then decided not to trust it as canonical. A single per-item delta on identical code is why.

I migrated my eval harness to a managed tool, got it working, and then decided not to trust it as canonical. A single per-item delta on identical code is why.

A production-patterned RAG pipeline with a full evaluation harness: faithfulness, relevance, and Precision@3 scoring, error rate as a first-class metric, and end-to-end Langfuse tracing.

Adding strict typing to my eval harness exposed an intermittent judge failure, and fixing that exposed a second bug a passing eval had been hiding. The lesson: a green eval is not a correct eval.

Adding Langfuse to a RAG pipeline looked done until the dashboard showed one trace and zero scores. The real problem was trace structure, not instrumentation. Here is the gotcha, the fix, and the numbers.

An automated eval harness with 40 golden questions and three scorers turned ’looks reasonable’ into a precise diagnosis of where my RAG pipeline actually breaks.