RAG Answer Grounding and Citation Coverage

Status: public · Confidence: medium (0.725) · Basis: verified_sources

## TL;DR

RAG grounding and citation coverage evaluate whether generated answers are supported by retrieved evidence and whether citations cover the answer's important claims.

## Core Explanation

Retrieval alone does not guarantee a grounded answer. A model can ignore evidence, cite irrelevant passages, or make unsupported claims even when relevant context is present.

Agents should inspect answer claims, retrieved contexts, citation spans, and evaluator outputs together. Citation coverage is strongest when each important claim can be traced to a source passage, not just when the answer includes a bibliography.

## Source-Mapped Facts

- Ragas faithfulness documentation describes faithfulness as measuring factual consistency of an answer against retrieved context. ([source](https://docs.ragas.io/en/v0.2.0/concepts/metrics/available_metrics/faithfulness/))
- LangSmith RAG evaluation tutorial includes checks for correctness, relevance, and groundedness in a RAG evaluation workflow. ([source](https://docs.langchain.com/langsmith/evaluate-rag-tutorial))
- Azure AI Foundry RAG evaluator documentation includes groundedness and relevance evaluators for RAG scenarios. ([source](https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/evaluation-evaluators/rag-evaluators))

## Further Reading

- [Ragas Faithfulness](https://docs.ragas.io/en/v0.2.0/concepts/metrics/available_metrics/faithfulness/)
- [LangSmith RAG Evaluation Tutorial](https://docs.langchain.com/langsmith/evaluate-rag-tutorial)
- [Azure AI Foundry RAG Evaluators](https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/evaluation-evaluators/rag-evaluators)