Why Evaluation Matters in RAG
Understand the two independent failure modes in RAG systems: retrieval failure and generation failure, and learn why you need separate metrics to diagnose each one.
You Cannot Improve What You Do Not Measure
A RAG system can feel like it is working because it returns fluent, plausible-sounding answers. But without measurement, you have no idea whether it is actually retrieving the right chunks or generating faithful answers. Teams that skip evaluation often spend months tweaking chunking strategies and prompt formats based on gut feeling, only to discover they made things worse. Rigorous evaluation is what turns RAG development from guessing into engineering.
Two Independent Failure Modes
RAG has two distinct stages that can fail independently: retrieval and generation. Retrieval fails when the relevant chunks are not ranked in the top-K results — the LLM cannot generate a good answer if the right information was never retrieved. Generation fails when the correct chunks were retrieved but the LLM ignored them, misread them, or added hallucinated information. You need separate metrics for each stage to pinpoint which component is causing the problem.