RAG & Knowledge Retrieval5.0 · 0 ratings

Faithfulness Auditor For RAG Outputs

Audits a generated answer against its source passages and flags every unsupported or contradicted claim.

Self-CritiqueChain-of-ThoughtStructured-Output

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. 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. 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. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Self-Critique

Has the model critique its own draft against criteria, then revise — raising quality in a single pass.

Learn this technique
Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Recommended models

claudegpt-4ogemini

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.

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