Granularity Mismatch

Issue: Citation Points to Wrong Level of Specificity

Frequency: Common

Symptoms

• Citation to entire document when specific section needed
• Page/section numbers missing or wrong
• User must search within document to find info
• Too-specific citation misses broader context

Root Cause Citations may be at document level when paragraph-level needed, or vice versa. Model may not track or output precise locations.

Example

Agent response: "The API rate limit is 100 requests per minute [1]"

Citation [1]: "Technical Documentation" (500-page PDF)

User experience: Must search 500 pages for rate limit info

Better citation: "Technical Documentation, Section 4.2.3: Rate Limits"

Mitigation Strategies

1. Hierarchical citations: Document > Section > Paragraph
2. Page/section tracking: Maintain location in chunk metadata
3. Deep linking: Link to specific section when possible
4. Quote inclusion: Include excerpt so user doesn’t need to search
5. Location metadata: Store and output precise locations
6. Chunk-level attribution: Cite specific chunk, not just document

Detection

• Track time users spend finding cited information
• Monitor citation specificity levels
• User feedback on citation usefulness
• Compare citation granularity to claim specificity

Mitigation Strategies

Prevention

1. Implement query-answer consistency validation: Decompose complex queries into atomic components and verify each component is addressed in the answer before returning. Use RAGAS Answer Relevancy metric (target: >0.75) with automatic re-generation for scores below threshold. Root cause mitigation: Prevents context-anchoring by explicitly binding answer generation to parsed query intent.

2. Apply multi-source consensus verification: Require answers synthesized from multiple sources to explicitly cite which sources support each claim and flag unresolved contradictions. Implementation: Use semantic similarity checks across source fragments to detect cherry-picked evidence patterns. Root cause: Ensures balanced representation of evidence when sources conflict.

3. Enforce comprehensive coverage checks: Implement structured extraction requiring explicit treatment of caveats, limitations, exceptions, and counterevidence for each claim type. Use template responses with mandatory caveat sections. Root cause: Prevents omission of qualifying information that would change user decision-making.

Detection & Response

1. Answer completeness monitoring: Measure coverage of query intents in generated answers. Track query decomposition rate (% of query components explicitly addressed) and flag responses with coverage <85%. Instrument RAG pipeline to log query-answer similarity scores per component. Alert on sustained scores <0.70.

2. Evidence balance scoring: For each answer, compute evidence distribution across sources and flag one-sided responses (>70% from single source on multi-source queries). Implement automated extraction of caveat/limitation mentions and track inclusion rates by query type. Target: >80% of medical/financial answers include relevant caveats.

Architecture Patterns

1. Query Intent Decomposition Graph: Parse complex queries into a DAG of atomic intents before retrieval. Each retrieved document is mapped to specific intent nodes. Answer generation must satisfy all leaf nodes. Validation layer computes coverage before response generation.

2. Evidence Consensus Engine: Maintain a fact graph where each claim is attributed to specific sources with confidence scores. Multi-source claims require consensus computation (intersection of sources supporting claim). Flagging layer surfaces contradictions to generation model.

3. Structured Response Templates: Use task-specific response schemas that enforce inclusion of: primary answer, supporting evidence, relevant caveats/exceptions, alternative interpretations, confidence bounds. Auto-flag template violations before user delivery.

Key Metrics

Alerts & Escalation

References

• Medium: 7 RAG Hallucination Root Causes - Chunking granularity
• FloTorch: 2026 RAG Performance Landscape - Citation precision