How to Evaluate RAG Systems? Key Metrics and Frameworks Used in Projects

In practice, evaluating a Retrieval‑Augmented Generation (RAG) project is crucial. The author uses the open‑source Ragas automated evaluation framework and splits evaluation into four dimensions: recall quality, answer faithfulness, answer relevance, and context utilization.

Ragas works by letting a large language model act as a judge: the user supplies the question, retrieved context, and the model‑generated answer, and Ragas scores the RAG output against preset metrics.

Answer Correctness

Measures whether the answer matches the reference answer in facts, conclusions, and key points. Core: is the result correct?

Answer Relevancy

Assesses whether the answer addresses the user’s actual question. Core: does it answer the asked point?

Faithfulness

Evaluates whether the answer is grounded in the provided context rather than hallucinated. Core: is the answer supported by the retrieved material?

Context Precision

Calculates the proportion of truly useful content among retrieved passages. Core: how precise is the retrieval, i.e., noise level.

Context Recall

Checks whether the retrieval returns all necessary evidence for answering the question. Core: how complete is the retrieval, i.e., missing key material.

The article includes diagrams illustrating precision versus recall trade‑offs. Covering both retrieval and generation metrics provides a comprehensive view of RAG quality.