Instruction Grounding Evidence Audit Prompt

This prompt is for regulated-industry engineering and governance teams who must prove that a model's factual claims are backed by evidence and governed by a specific instruction. The primary job-to-be-done is producing an audit artifact that connects each claim in a generated response to its source instruction, cites the provided evidence, and explicitly flags unsupported statements for human review. Use this when you are preparing a compliance submission, responding to a model behavior inquiry, or building a continuous evidence-grounding monitor into your AI pipeline.

You should not use this prompt for real-time, low-latency chat moderation or as a substitute for a structured retrieval-augmented generation (RAG) pipeline. It is a post-hoc audit and traceability tool, not a real-time guardrail. The ideal user is a compliance engineer, AI safety reviewer, or technical auditor who has access to the full model response, the active system and policy instructions, and the evidence context that was available at generation time. The prompt assumes you can provide these inputs; if you cannot reconstruct the exact instruction set and evidence context, the audit output will be incomplete.

Before running this prompt, ensure you have captured the [FULL_MODEL_RESPONSE], the [ACTIVE_INSTRUCTIONS] (including system, policy, and tool-use rules), and the [EVIDENCE_CONTEXT] (retrieved documents, tool outputs, or data payloads). The prompt will produce a structured claim-by-claim trace. For high-stakes regulatory use, the output must be reviewed by a human who can verify that the identified evidence genuinely supports each claim. Do not treat the audit output as a self-certification; it is a structured input for human judgment and governance logging.

This template is the core of the Instruction Grounding Evidence Audit workflow. It accepts a model-generated response, the set of active instructions that governed its production, and any source documents or tool outputs available at generation time. The prompt instructs the auditing model to extract each factual claim, map it to the instruction that required or permitted it, cite the specific evidence supporting it, and flag claims that lack sufficient grounding. Use this template when you need to produce an audit artifact that proves which rules were active and whether the output adhered to them—critical for regulated industries, compliance reviews, and production debugging.

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You are an instruction grounding auditor. Your task is to examine a model-generated response and determine whether each factual claim is grounded in evidence and consistent with the active instructions that governed its production.

## INPUTS

### MODEL RESPONSE
[RESPONSE]

### ACTIVE INSTRUCTIONS
Provide the full text of all instructions active during generation, labeled by layer and priority.
[ACTIVE_INSTRUCTIONS]

### SOURCE EVIDENCE
Provide all documents, tool outputs, retrieved passages, or data available to the model at generation time.
[SOURCE_EVIDENCE]

### AUDIT CONFIGURATION
- Risk Level: [RISK_LEVEL]
- Required Confidence Threshold: [CONFIDENCE_THRESHOLD]
- Flag Unsupported Claims For: [REVIEW_ACTION]

## OUTPUT SCHEMA

Return a JSON object with the following structure:
{
  "audit_metadata": {
    "audit_id": "string",
    "response_timestamp": "string",
    "instruction_version": "string",
    "total_claims_extracted": number,
    "claims_fully_grounded": number,
    "claims_partially_grounded": number,
    "claims_unsupported": number,
    "overall_grounding_score": number
  },
  "claims": [
    {
      "claim_id": "string",
      "claim_text": "string",
      "claim_type": "factual_assertion | interpretation | recommendation | value_statement",
      "governing_instruction": {
        "instruction_text": "string",
        "instruction_layer": "system | developer | user | tool | policy",
        "instruction_priority": number
      },
      "evidence_assessment": {
        "grounding_status": "fully_grounded | partially_grounded | unsupported | not_applicable",
        "supporting_evidence": [
          {
            "source_id": "string",
            "source_excerpt": "string",
            "relevance": "direct | indirect | weak",
            "confidence": number
          }
        ],
        "evidence_gap_description": "string or null"
      },
      "requires_human_review": boolean,
      "review_reason": "string or null"
    }
  ],
  "instruction_compliance_summary": {
    "instructions_evaluated": number,
    "instructions_fully_satisfied": number,
    "instructions_partially_satisfied": number,
    "instructions_violated": number,
    "violations": [
      {
        "instruction_text": "string",
        "violation_description": "string",
        "affected_claim_ids": ["string"]
      }
    ]
  }
}

## CONSTRAINTS

1. Extract every factual claim from the response, including implicit claims embedded in recommendations or interpretations.
2. For each claim, identify the specific instruction that required, permitted, or constrained that claim. If no instruction governs the claim, note this as a potential instruction gap.
3. Match each claim to evidence in the source documents. If evidence is missing, incomplete, or only tangentially related, flag the gap explicitly.
4. Do not invent evidence. If a claim cannot be traced to provided sources, mark it as unsupported.
5. For claims marked unsupported or partially grounded, set requires_human_review to true and provide a clear review_reason.
6. If the risk level is "high" or "critical", err on the side of flagging for review rather than accepting weak evidence.
7. Preserve exact source excerpts in the supporting_evidence array. Do not paraphrase evidence.
8. If the model response contains no factual claims (e.g., purely conversational or procedural output), return an empty claims array with appropriate metadata.

To adapt this template for your environment, replace the square-bracket placeholders with your actual data at runtime. [RESPONSE] should contain the full model output under audit. [ACTIVE_INSTRUCTIONS] must include every instruction layer active during generation—system prompts, developer messages, user directives, tool output instructions, and policy constraints—each labeled with its layer and priority. [SOURCE_EVIDENCE] should contain the complete set of documents, retrieved passages, API responses, or database records available to the model. Set [RISK_LEVEL] to "low", "medium", "high", or "critical" to control the audit's sensitivity. [CONFIDENCE_THRESHOLD] defines the minimum evidence confidence score (0.0–1.0) required to consider a claim grounded. [REVIEW_ACTION] specifies what to do with flagged claims: "queue_for_human", "block_output", or "log_only". For high-stakes regulated workflows, always set risk_level to "high" or "critical" and review_action to "queue_for_human".

Before deploying this prompt into production, validate that your instruction labeling is consistent across all layers. If system instructions reference policy documents by name but those documents aren't included in [SOURCE_EVIDENCE], the audit will produce false unsupported flags. Test the prompt against a golden dataset of responses with known grounding status—include examples where claims are fully grounded, partially grounded, and completely unsupported—to calibrate your confidence threshold. For regulated industries, retain the full audit output JSON alongside the original response and active instructions as part of your compliance evidence package. Do not use this prompt as a real-time guardrail on every generation; it is designed for post-hoc audit sampling, incident investigation, and periodic compliance review, not for blocking outputs at inference time.

The Instruction Grounding Evidence Audit Prompt is not a one-shot conversational tool—it is a compliance-grade audit component that must be embedded in a deterministic, observable pipeline. The prompt expects a structured input containing the original model response, the active instruction set (with version metadata), and any evidence or tool outputs that were available at generation time. The output is a structured audit record that maps each factual claim to its governing instruction and supporting evidence, flags unsupported statements, and assigns a grounding confidence score. Because this output may be used in regulatory submissions or internal governance reviews, the implementation harness must enforce schema validation, handle model non-determinism, and preserve an immutable audit trail of the audit itself.

Pipeline Integration: Wire this prompt as a post-generation audit step that runs after the primary model response is produced but before the response is delivered to the user or written to the system of record. The harness should assemble the input payload from three sources: (1) the original model response text, (2) the active instruction stack with version hashes and layer labels (system, developer, user, tool, policy), and (3) the evidence corpus or tool outputs that were in context during generation. If your system uses RAG, include the retrieved passages with their source identifiers. If tool calls were made, include the raw tool outputs, not just the model's summary of them. The prompt's [CLAIMS] placeholder should be populated by first extracting discrete factual claims from the response using a lightweight extraction step—this separation improves audit precision compared to asking the model to both extract and evaluate claims in one pass.

Validation and Retry Logic: The output schema is strict: each claim must have a claim_text, governing_instruction_id, instruction_layer, evidence_citation, grounding_status (one of GROUNDED, PARTIALLY_GROUNDED, UNSUPPORTED, OPINION), and confidence_score. Implement a JSON schema validator in your harness that rejects malformed outputs before they enter the audit log. If validation fails, retry with the same input up to two times, appending the validation error message to the prompt as a [PREVIOUS_ERROR] constraint. If the third attempt still fails, escalate to a human reviewer with the raw model output and validation errors attached. For high-risk domains (healthcare, legal, finance), configure the harness to always route UNSUPPORTED and PARTIALLY_GROUNDED claims to a human review queue before the audit record is finalized. The human reviewer should be able to override the grounding status, add notes, and sign off on the final record.

Model Choice and Logging: Use a model with strong instruction-following and structured output capabilities—GPT-4o, Claude 3.5 Sonnet, or equivalent—with a low temperature setting (0.0–0.2) to minimize creative variance in audit judgments. Log every audit invocation as an immutable record: input payload hash, model version, prompt template version, raw output, validation result, retry count, human review actions, and final signed audit record. Store these logs in a write-once data store that supports downstream traceability queries. If your system processes high volumes, consider batching multiple responses into a single audit invocation to reduce API costs, but ensure each response's audit remains independently addressable in the log. Never use the audit prompt's output to automatically suppress or modify the original response—the audit is an observation layer, not a control layer.

What to Avoid: Do not skip the claim extraction pre-processing step and feed the raw response directly into the [CLAIMS] placeholder—the model will often miss claims or produce vague groupings that weaken the audit's evidentiary value. Do not treat the audit prompt as a real-time guardrail that blocks responses; its latency profile and occasional need for human review make it better suited as an asynchronous governance layer. Finally, do not assume the audit output is ground truth—it is itself a model-generated artifact that must be sampled, reviewed, and calibrated against human auditor judgments as part of your evaluation program. The harness should include a periodic calibration workflow where human auditors review a random sample of audit records and flag discrepancies, feeding those corrections back into prompt improvements and model selection decisions.