> tpl_air_031
Grounding, Citation and Source-Attribution Policy
Strict epistemic governance and transparency policy for enterprise generative AI and RAG assistants, standardizing factual grounding thresholds, zero-unsupported claim mandates, deterministic refusal protocols when context is missing, inline citation formats, and source verification audit logs.
AI grounding policy establishing zero-unsupported claim rules, inline citations, context refusal protocols, and faithfulness audits.
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Problem Solved
Enterprise RAG bots generate plausible-sounding hallucinations without citations or manufacture fake references, exposing the company to severe legal liability, customer distrust, and compliance violations.
When to Use
- •Establishing enterprise-wide policy standards for factual accuracy in customer-facing and employee-facing AI assistants
- •Configuring prompt templates to enforce inline citation brackets ([Source: Doc A, Page 12]) for every substantive factual claim
- •Enforcing strict graceful refusal behaviors when the retrieved context does not contain the answer
When NOT to Use
- •For measuring statistical information retrieval metrics (Recall@k, NDCG@k) in the vector database (use TPL-AIR-030)
- •For implementing access control filtering and tenant isolation on the vector index (use TPL-AIR-033)
5 Template Sections & Structural Outline
Codifying the primary epistemic rule: The model must never generate a substantive factual claim that cannot be directly mapped to an explicitly provided retrieved context chunk.
Mandating strict bracketed citations: Every paragraph or distinct factual clause must cite its exact source (e.g. "[Ref 1: SOC-2-Report-2026.pdf#p14]"). Providing clickable deep-links in user interfaces to the original source document.
Establishing explicit refusal protocols: When the retrieved context is empty, low-relevance (< 0.65), or contradictory, the assistant must state: "I cannot find sufficient verified information in internal sources to answer this question."
Deploying automated LLM-as-a-judge evaluation pipelines: calculating Faithfulness Score (claims supported / total claims). Enforcing a minimum production threshold of > 0.95 faithfulness.
Recording an immutable audit payload for every generated response: { query, retrieved_chunk_ids, prompt_tokens, generated_answer, cited_sources, faithfulness_score, timestamp } for regulatory proof.
Completion Instructions
Independent Review Checklist
- All mandatory sections completed
- No secrets or passwords included
- Executive sponsor sign-off obtained
Grounding, Citation and Source-Attribution Policy - Worked Case Study
Fictional Entity: Enterprise Healthcare Clinical Decision Support Platform
Real-world production case study demonstrating complete operational adoption for Enterprise Healthcare Clinical Decision Support Platform.
- •Eliminated unsupported clinical recommendations by enforcing mandatory bracketed PubMed/Internal trial citations
- •Achieved a 98.4% Ragas faithfulness score across 50,000 monthly enterprise diagnostic search interactions
- •Configured deterministic refusal protocols that correctly declined 100% of out-of-domain medical queries
Frequently Asked Questions
What is the mathematical definition of "Faithfulness" in RAG evaluation frameworks?
Faithfulness evaluates whether the claims in the generated response can be directly inferred from the retrieved context. It is calculated by extracting all independent factual statements from the generated text and checking whether each statement is logically entailed by the context chunks: Faithfulness = (Number of Entailed Claims) / (Total Number of Extracted Claims). A score of 1.0 means 100% zero hallucination.
Why should models be trained or prompted to refuse rather than approximate an answer?
In enterprise, healthcare, financial, and legal contexts, an incorrect answer presented with high confidence is exponentially more destructive than a polite refusal. Saying "I do not have sufficient information to answer" preserves institutional trust, prevents catastrophic business decisions, and directs users to human experts.
How can prompt engineering enforce strict inline citation formatting?
System prompts must include few-shot examples showing exact desired citation brackets, an explicit negative constraint ("Never state a fact without appending its citation [Source: ID]"), and instruction to group citations at the end of the sentence. Combining this with constrained output schemas or regex validators guarantees 100% compliance.
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Authoritative Sources
- EU Artificial Intelligence Act - Article 13: Transparency and Provision of InformationEuropean Parliament • OFFICIAL REQUIREMENT
- Ragas: Automated Evaluation of Retrieval Augmented Generation SystemsExploding Gradients • OFFICIAL REQUIREMENT
- Evaluating Factual Consistency and Faithfulness in Abstractive SummarizationKryscinski et al. • OFFICIAL REQUIREMENT