---
id: rag-groundedness-and-faithfulness-evaluation
title: 'RAG Groundedness and Faithfulness Evaluation'
schema_type: TechArticle
category: ai
language: en
confidence: medium
last_verified: '2026-06-02'
created_date: '2026-06-02'
generation_method: ai_structured
derived_from_human_seed: true
conflict_of_interest: none_declared
is_live_document: false
data_period: static
atomic_facts:
  - id: fact-ai-rag-groundedness-and-faithfulness-evaluation-1
    statement: >-
      Ragas documentation defines faithfulness as measuring factual consistency of a generated answer
      against the given context.
    source_title: Ragas Faithfulness
    source_url: https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/faithfulness/
    confidence: medium
  - id: fact-ai-rag-groundedness-and-faithfulness-evaluation-2
    statement: >-
      LlamaIndex evaluating documentation lists response evaluation and retrieval evaluation as
      evaluation areas for LlamaIndex applications.
    source_title: LlamaIndex Evaluating
    source_url: https://developers.llamaindex.ai/python/framework/module_guides/evaluating/
    confidence: medium
  - id: fact-ai-rag-groundedness-and-faithfulness-evaluation-3
    statement: >-
      Phoenix faithfulness documentation says its evaluator checks whether an LLM response is
      grounded in and faithful to the provided context.
    source_title: Phoenix Faithfulness
    source_url: https://arize.com/docs/phoenix/evaluation/pre-built-metrics/faithfulness
    confidence: medium
completeness: 0.84
known_gaps:
  - Faithfulness scores depend on evaluator prompts, judge models, context formatting, and whether retrieved context itself is correct.
disputed_statements: []
primary_sources:
  - title: Ragas Faithfulness
    type: documentation
    year: 2026
    url: https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/faithfulness/
    institution: Ragas
  - title: LlamaIndex Evaluating
    type: documentation
    year: 2026
    url: https://developers.llamaindex.ai/python/framework/module_guides/evaluating/
    institution: LlamaIndex
  - title: Phoenix Faithfulness
    type: documentation
    year: 2026
    url: https://arize.com/docs/phoenix/evaluation/pre-built-metrics/faithfulness
    institution: Arize Phoenix
secondary_sources: []
updated: '2026-06-02'
ai_models:
  - gpt-5-codex
---

## TL;DR

Groundedness and faithfulness evaluation asks whether a RAG answer is supported by the retrieved context, not merely whether it sounds plausible.

## Core Explanation

RAG systems can fail even when retrieval returns relevant documents. The model may add unsupported details, contradict a retrieved passage, or answer from prior knowledge instead of context. Faithfulness evaluation targets that gap by comparing output against supplied evidence.

For production use, groundedness should be paired with retrieval relevance, citation checks, and regression datasets. A high faithfulness score on irrelevant context can still produce an unhelpful answer, while a relevant context set can still be misused by the generator.

## Source-Mapped Facts

- Ragas documentation defines faithfulness as measuring factual consistency of a generated answer against the given context. ([source](https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/faithfulness/))
- LlamaIndex evaluating documentation lists response evaluation and retrieval evaluation as evaluation areas for LlamaIndex applications. ([source](https://developers.llamaindex.ai/python/framework/module_guides/evaluating/))
- Phoenix faithfulness documentation says its evaluator checks whether an LLM response is grounded in and faithful to the provided context. ([source](https://arize.com/docs/phoenix/evaluation/pre-built-metrics/faithfulness))

## Further Reading

- [Ragas Faithfulness](https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/faithfulness/)
- [LlamaIndex Evaluating](https://developers.llamaindex.ai/python/framework/module_guides/evaluating/)
- [Phoenix Faithfulness](https://arize.com/docs/phoenix/evaluation/pre-built-metrics/faithfulness)