A Practical Guide to Evaluating RAG Systems: Metrics That Matter

Author(s): Ajit Kumar Singh

Originally published on Towards AI.

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Retrieval-Augmented Generation (RAG) revolutionizes how language models ground their answers in external data. By combining a retriever that fetches relevant information from a knowledge base and a generator that creates responses using that information, RAG systems enable more accurate and trustworthy outputs.

But how do you evaluate a RAG system? How do you know if it’s retrieving the right context or generating reliable answers?

This guide breaks it all down with practical metrics, worked examples, and actionable insights.

Two core components:

Retriever: Pulls relevant chunks of information (context) from a vector database.Generator: Uses the context to generate a coherent, factual response.

Each stage needs its own set of metrics for proper evaluation. Let’s explore them.

The retriever is the first critical component in any RAG (Retrieval-Augmented Generation) system. Its job? To fetch the most relevant and helpful pieces of information from a vector database in response to an input query.

To assess how well it’s doing, we rely on three core metrics:

Contextual PrecisionContextual RecallContextual Relevancy

Let’s explore each one, starting with Contextual Precision.

Contextual Precision measures whether the most relevant context nodes (document chunks) are ranked higher than irrelevant ones. It’s not just about what was retrieved, but how well it was ranked.A high Contextual… Read the full blog for free on Medium.

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Published via Towards AI