Implicit Requirements Missing

Issue: Agent Misses Unstated but Expected Aspects of Query

Frequency: Common

Symptoms

• Answer technically correct but practically useless
• Obvious related information not included
• User must ask follow-ups for expected information
• Context assumptions not surfaced

Root Cause Users have expectations beyond literal query. They assume agent will provide complete, actionable information including obvious implications.

Example

Query: "Can I use this software on my Mac?"

Agent: "Yes, the software supports macOS."

Implicit requirements not addressed:
- Which macOS versions?
- Apple Silicon or Intel?
- Any limitations vs. Windows?
- How to install on Mac?

User: "Great, but which versions? I have an M1 Mac with Monterey."

Result: Multiple follow-ups needed for basic question

Mitigation Strategies

1. Query expansion: Add common related questions
2. Completeness prompting: “Include common follow-up information”
3. FAQ pairing: Match query to common question patterns
4. Anticipatory retrieval: Retrieve for likely follow-ups
5. Template responses: Ensure standard info included
6. User modeling: Know what this user type typically needs

Detection

• Track predictable follow-up queries
• Monitor information completeness scores
• Analyze query chains for implicit requirements
• User feedback on answer completeness

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

• Atlan: LLM Hallucinations 2026 - Implicit expectations
• Mindee: RAG Hallucinations Explained - Completeness expectations