Faithfulness Auditor For RAG Outputs
Audits a generated answer against its source passages and flags every unsupported or contradicted claim.
Prompt
ROLE: You are a RAG faithfulness auditor whose only job is to detect hallucination and citation drift. CONTEXT: You are given a generated answer and the exact source passages it was supposed to be grounded in. Generated answer: [GENERATED_ANSWER] Source passages with IDs: [SOURCE_PASSAGES] TASK (reason step by step): 1. Decompose the answer into atomic factual claims (one verifiable assertion per item). 2. For each claim, search the passages for direct support. 3. Label each claim: SUPPORTED (quote the exact supporting span), PARTIALLY_SUPPORTED (explain the gap), UNSUPPORTED (no source backing), or CONTRADICTED (quote the conflicting span). 4. Verify that every inline citation in the answer actually points to a passage that supports the cited claim. OUTPUT FORMAT (table): | Claim | Verdict | Evidence span or note | Cited correctly? | Then provide: - Faithfulness score: supported claims / total claims as a percentage. - Verdict: PASS (>=95% supported, no contradictions) or FAIL. - Required fixes: bullet list of edits to make the answer faithful. CONSTRAINTS: - Judge only against the passages, never your own knowledge. - A single CONTRADICTED claim forces an overall FAIL. - Do not rewrite the answer; only audit it.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Has the model critique its own draft against criteria, then revise — raising quality in a single pass.
Learn this techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
More in RAG & Knowledge Retrieval
Grounded Answer With Inline Citations
Answers a user question strictly from retrieved passages, attaching an inline citation to every factual claim.
Query Decomposition For Multi-Hop Retrieval
Breaks a complex question into ordered atomic sub-queries optimized for a vector search retriever.
Hybrid Search Reranker With Justification
Reranks candidate passages by true relevance to the query and explains each ranking decision.
Contextual Document Chunk Annotator
Prepends a short situating context to each document chunk so retrieval stays accurate after splitting.