Synthesis Errors

Issue: Model Incorrectly Combines Information from Multiple Sources

Frequency: Common

Symptoms

• Facts from different entities merged incorrectly
• Temporal mixing (past/present confused)
• Attributes assigned to wrong subjects
• Calculations or aggregations wrong

Root Cause When synthesizing from multiple sources, model may incorrectly associate facts with wrong entities or contexts.

Example

Retrieved context:

Doc 1: "Product A: Price $99, ships in 2-3 days"
Doc 2: "Product B: Price $149, includes free installation"

Query: "Tell me about Product A"

Agent: "Product A costs $99 and includes free installation, 
shipping in 2-3 days."

Error: Free installation is from Product B, not A

Result: User expects installation that won't be provided

Mitigation Strategies

1. Entity tracking: Maintain clear entity-attribute associations
2. Structured extraction: Extract to schema before synthesizing
3. Single-source answers: Prefer one authoritative source per fact
4. Attribution requirements: Cite source for each claim
5. Validation checks: Verify synthesized facts against sources
6. Chain-of-thought: Show reasoning for synthesis

Detection

• Cross-check synthesized facts against individual sources
• Track entity-attribute assignment accuracy
• Monitor multi-source synthesis errors
• User reports of mixed-up information

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

• Mindee: RAG Hallucinations Explained - Synthesis failures
• FloTorch: 2026 RAG Performance Landscape - Multi-source synthesis