Clinical RAG Failure Classification System
Catching retrieval failures before a clinician sees them
A diagnostic layer that sits inside a medical retrieval-augmented generation pipeline and classifies why a given answer might be untrustworthy — missing context, irrelevant retrieval, or contradicted-by-source generation — instead of only scoring the final answer.
features
- · Medical document retrieval over a clinical corpus
- · A dedicated retrieval-failure detection model, separate from the generator
- · End-to-end RAG pipeline with swappable retriever/generator components
- · LLM integration for answer synthesis with failure-aware fallbacks
Challenge — Most RAG evaluation only scores the final answer, which hides whether a wrong answer came from bad retrieval or bad generation. Building a labeled failure-mode dataset and a classifier that separates the two was the core technical problem.
Impact — Gives a concrete, inspectable reason when a clinical RAG answer should be trusted less — a step toward RAG systems that fail loudly instead of silently in high-stakes domains.