Evaluating RAG Systems

A practical guide to measuring retrieval and generation separately, building datasets you can trust, and turning one-off checks into a regression process.

RAG systems fail politely: in complete sentences, with total confidence, in a way that looks exactly like success. This guide is the method for catching that before your users do, from the first honest run to a gated, monitored evaluation pipeline.

Read our in-depth guide to:

• Diagnose failures by stage instead of guessing at the prompt
• Measure retrieval and generation with metrics that localize the bug
• Calibrate LLM judges and understand what their scores actually mean
• Build golden and living datasets, including traps and metamorphic pairs
• Red team a RAG product, where the corpus itself is an attack surface
• Run it all on schedules and pull requests, fed by production traffic