RAG Evaluation Metrics: Measuring What Actually Matters

Building a RAG system is one thing. Knowing whether it works well is another entirely.

You need metrics that tell you if your retrieval finds the right documents, if your context contains useful information, and if your generated answers are actually correct. Without proper evaluation, you’re flying blind.

RAG evaluation splits into two categories: retrieval metrics that measure how well you find relevant context, and generation metrics that measure how well you produce answers from that context.

The Evaluation Framework

Every RAG metric involves comparing components of your system against some reference.

Query: What the user asks.

Ground Truth: The correct, verified answer or the known relevant documents.

Context: What your retrieval system finds and provides to the language model.

Response: What your language model generates based on the context.

Different metrics emphasize different relationships between these components. Understanding which component matters most for each metric helps you diagnose where your system needs improvement.

Retrieval Metrics: Finding the Right Context

Retrieval metrics evaluate whether your system finds relevant documents. You can have the best language model in the world, but if you feed…