Multi-Part Query Fragmentation

Issue: Agent Only Addresses Part of Multi-Part Question

Frequency: Common

Symptoms

• Only first question in query answered
• Later parts of question ignored
• User must re-ask for remaining parts
• Incomplete response to compound question

Root Cause Model attention focuses on early parts of query, or retrieval optimizes for one part, missing others.

Example

Query: "What are the system requirements, pricing tiers, and 
implementation timeline for the Enterprise plan?"

Agent response: "The Enterprise plan requires minimum 16GB RAM, 
4-core CPU, and 100GB storage. The software runs on Windows, 
Mac, and Linux."

Missing:
- Pricing tiers (not mentioned)
- Implementation timeline (not mentioned)

Result: User must ask two more times for complete answer

Mitigation Strategies

1. Query decomposition: Split multi-part queries into sub-queries
2. Checklist approach: Enumerate parts, ensure each addressed
3. Structured responses: Use sections for each query part
4. Coverage verification: Check all query components in answer
5. Sequential retrieval: Retrieve for each part separately
6. Answer completeness prompt: “Ensure you address all parts”

Detection

• Parse queries for multiple question marks or conjunctions
• Compare query parts to answer coverage
• Track follow-up queries for missing parts
• Measure multi-part query completion rate

Mitigations

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

Metric	Target	Alert Threshold	Measurement Method
Answer Relevancy Score	>0.75	<0.70	RAGAS metric on generated answers vs. original query
Query Coverage Rate	>90%	<85%	Percentage of query components explicitly addressed in answer
Evidence Balance Index	>0.6	<0.4	Distribution of citations across sources (Gini coefficient, 0=balanced, 1=single-source)
Caveat Inclusion Rate	>80%	<70%	Percentage of medical/financial answers including relevant limitations
User Clarification Rate	<5%	>10%	Percentage of answered queries requiring follow-up clarification

Alerts & Escalation

Alert	Condition	Severity	Response
Low Answer Relevancy	Answer Relevancy Score < 0.70 for >5% of queries in 1-hour window	HIGH	Page on-call; trigger re-generation with query reinforcement prompt
Single-Source Dominance	Evidence Balance Index < 0.4 on multi-source queries for >3 consecutive queries	MEDIUM	Log event; audit cherry-picking patterns in retrieval/synthesis
Rising Clarification Demand	User Clarification Rate exceeds 10% (vs. 5% baseline) over 24-hour window	HIGH	Investigate query decomposition or answer template effectiveness

References

• FloTorch: 2026 RAG Performance Landscape - Complex query handling
• Atlan: LLM Hallucinations 2026 - Query decomposition failures