Evidence-Mapped Retrieval: Why Citations Matter in Enterprise AI

"The AI said it, so it must be true." That doesn't work in enterprises. When your legal team asks where an answer came from, when auditors need proof, when users demand verification—you need traceable citations. Evidence-mapped retrieval makes every AI answer provable. Here's how to implement it.

What is Evidence-Mapped Retrieval?

Evidence-mapped retrieval is a RAG pattern where every claim in an AI response is explicitly linked to source documents. Instead of just generating an answer, the system extracts citations, calculates confidence scores, and provides an audit trail showing exactly which documents support each statement.

1. Why Citations Matter

The Trust Problem

User: "Can we terminate this contract early?"
AI: "Yes, you can terminate with 30 days notice."

User: "Where does it say that?"
AI: "..." 😬

Without citations, users can't verify. Auditors can't audit. Legal can't rely on it.

Enterprise Requirements

The Business Case

• Reduced escalations: Users verify without asking humans
• Audit readiness: Pass SOC 2 / HIPAA inspections
• User adoption: Trust drives usage
• Error detection: Wrong citations reveal hallucinations

2. The Evidence-Mapping Pattern

Standard RAG Response

{
    "answer": "The contract can be terminated with 30 days written notice to the other party."
}

Evidence-Mapped Response

{
    "answer": "The contract can be terminated with 30 days written notice to the other party.",
    "citations": [
        {
            "claim": "terminated with 30 days written notice",
            "source": {
                "document_id": "CONTRACT-2024-001",
                "section": "Section 8.2 - Termination",
                "page": 12,
                "text": "Either party may terminate this Agreement upon thirty (30) days prior written notice to the other party."
            },
            "confidence": 0.95,
            "match_type": "exact"
        }
    ],
    "metadata": {
        "sources_consulted": 5,
        "sources_cited": 1,
        "overall_confidence": 0.95
    }
}

3. Citation Extraction Methods

Method 1: Inline Citation (LLM-Generated)

CITATION_PROMPT = """Answer the question using only the provided documents.
For each claim, include a citation in brackets like [Doc1, Section 2.3].

Documents:
{documents}

Question: {question}

Answer with inline citations:"""

def generate_with_citations(query: str, documents: list) -> str:
    docs_text = "\n\n".join([
        f"[Doc{i+1}] {doc['title']}\n{doc['content']}"
        for i, doc in enumerate(documents)
    ])
    
    response = anthropic.messages.create(
        model="claude-3-sonnet-20240229",
        max_tokens=1000,
        messages=[{
            "role": "user",
            "content": CITATION_PROMPT.format(
                documents=docs_text,
                question=query
            )
        }]
    )
    
    return response.content[0].text

Output:

The contract can be terminated with 30 days written notice [Doc1, Section 8.2]. 
Both parties must provide notice in writing [Doc1, Section 8.2].

Method 2: Post-Generation Attribution

def attribute_claims(answer: str, documents: list) -> list:
    sentences = split_into_sentences(answer)
    citations = []
    
    for sentence in sentences:
        best_match = None
        best_score = 0
        
        for doc in documents:
            score = compute_similarity(sentence, doc["content"])
            if score > best_score:
                best_score = score
                best_match = doc
        
        if best_score > 0.7:
            citations.append({
                "claim": sentence,
                "source": {
                    "document_id": best_match["id"],
                    "text": find_matching_excerpt(sentence, best_match["content"])
                },
                "confidence": best_score
            })
        else:
            citations.append({
                "claim": sentence,
                "source": None,
                "confidence": best_score,
                "warning": "Low confidence - may be model knowledge"
            })
    
    return citations

Method 3: Structured Extraction (Best for Enterprise)

STRUCTURED_PROMPT = """Answer the question using the provided documents.

Return your response as JSON with this exact structure:
{
    "answer": "Your complete answer here",
    "claims": [
        {
            "statement": "A specific claim from your answer",
            "source_doc": "Document ID",
            "source_section": "Section/page reference",
            "source_quote": "Exact quote supporting this claim",
            "confidence": 0.0-1.0
        }
    ]
}

Documents:
{documents}

Question: {question}

JSON Response:"""

4. Confidence Scoring

Confidence Factors

Implementation

def calculate_confidence(
    claim: str,
    source_text: str,
    document_metadata: dict,
    retrieval_rank: int
) -> float:
    semantic_score = compute_similarity(claim, source_text)
    
    claim_words = set(claim.lower().split())
    source_words = set(source_text.lower().split())
    lexical_score = len(claim_words & source_words) / len(claim_words | source_words)
    
    authority_scores = {
        "official_policy": 1.0,
        "contract": 0.95,
        "internal_doc": 0.8,
        "email": 0.6,
        "draft": 0.4
    }
    authority_score = authority_scores.get(document_metadata.get("type"), 0.5)
    
    rank_score = 1.0 / (retrieval_rank + 1)
    
    confidence = (
        0.4 * semantic_score +
        0.2 * lexical_score +
        0.2 * authority_score +
        0.2 * rank_score
    )
    
    return min(1.0, max(0.0, confidence))

Confidence Thresholds

5. Building Audit Trails

Audit Log Schema

@dataclass
class AuditEntry:
    timestamp: datetime
    query_id: str
    user_id: str
    query_text: str
    documents_retrieved: List[str]
    documents_cited: List[str]
    answer_generated: str
    citations: List[dict]
    overall_confidence: float
    model_used: str
    latency_ms: int

class AuditTrail:
    def __init__(self, storage):
        self.storage = storage
    
    def log_query(self, entry: AuditEntry):
        self.storage.write({
            "timestamp": entry.timestamp.isoformat(),
            "query_id": entry.query_id,
            "user_id": entry.user_id,
            "query_text": entry.query_text,
            "documents_retrieved": entry.documents_retrieved,
            "documents_cited": entry.documents_cited,
            "answer": entry.answer_generated,
            "citations": entry.citations,
            "confidence": entry.overall_confidence,
            "model": entry.model_used,
            "latency_ms": entry.latency_ms
        })

6. Implementation Code

Complete Evidence-Mapped RAG

class EvidenceMappedRAG:
    def __init__(self, retriever, llm, audit_trail):
        self.retriever = retriever
        self.llm = llm
        self.audit_trail = audit_trail
    
    def query(self, query: str, user_id: str) -> dict:
        query_id = generate_id()
        start_time = time.time()
        
        documents = self.retriever.search(query, top_k=10)
        result = self.generate_with_citations(query, documents)
        
        for citation in result["claims"]:
            citation["confidence"] = calculate_confidence(
                citation["statement"],
                citation["source_quote"],
                documents[citation["source_doc"]],
                citation.get("retrieval_rank", 0)
            )
        
        confidences = [c["confidence"] for c in result["claims"] if c.get("source_doc")]
        result["overall_confidence"] = np.mean(confidences) if confidences else 0.0
        
        self.audit_trail.log_query(AuditEntry(
            timestamp=datetime.now(),
            query_id=query_id,
            user_id=user_id,
            query_text=query,
            documents_retrieved=[d["id"] for d in documents],
            documents_cited=[c["source_doc"] for c in result["claims"] if c.get("source_doc")],
            answer_generated=result["answer"],
            citations=result["claims"],
            overall_confidence=result["overall_confidence"],
            model_used="claude-3-sonnet",
            latency_ms=int((time.time() - start_time) * 1000)
        ))
        
        return result

7. User Experience Design

Citation Display

Confidence Indicators

Next Steps

1. GraphRAG Implementation Guide → - Full architecture for enterprise knowledge systems
2. Enterprise RAG Security → - RBAC and compliance controls
3. Hybrid Search Implementation → - Better retrieval for better citations

---

Need help implementing evidence-mapped retrieval?

At Cognilium, we built Legal Lens AI with 95% citation accuracy on 1.2M contracts. Let's discuss your requirements →