Static RAG eval sets go stale: treat your eval dataset as a pipeline, not a file
paulabartabajo_ · x · 2026-10-30
The author argues that most teams evaluate RAG systems with a static set of (question, answer) pairs, which breaks in production because knowledge bases keep changing as documents are added, updated, or deleted.
The fix: treat the eval dataset as a pipeline rather than a static asset — auto-generate it from the knowledge base, regenerate on every knowledge-base change, and re-run evaluations on the fresh dataset. An accompanying diagram shows the full architecture: knowledge base plus diverse input queries feeding a golden-dataset generation pipeline (which also flags knowledge-base issues), consumed by an evaluation pipeline for the support agent.
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