evidence-based-rag

name	evidence-based-rag
description	Evidence-first retrieval-augmented reasoning skill for decision-critical scenarios. Retrieves information from a specified knowledge base, extracts verifiable evidence, detects conflicting claims, and evaluates answer sufficiency with explicit confidence and risk signals. Produces traceable outputs suitable for agent-level decision control and escalation.

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Evidence-Based RAG

Overview

evidence_based_rag is an evidence-first retrieval-augmented reasoning skill designed to retrieve, extract, and organize factual information from a specified knowledge base in support of decision-critical scenarios (e.g., due diligence, compliance review, research analysis).

The responsibility of this skill is to produce traceable, verifiable, and auditable findings, while explicitly surfacing conflicts and uncertainty signals. It does not perform business judgments or replace human decision-making.

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Skill Responsibility

This skill is responsible for:

• Retrieving relevant information from a specified knowledge base
• Transforming raw text into verifiable evidence units
• Detecting conflicting claims related to the same factual question
• Evaluating answer sufficiency and confidence
• Explicitly surfacing uncertainty and risk signals, along with suggested follow-up actions

This skill is not responsible for:

• Arbitrating between conflicting claims
• Making investment, legal, or business decisions
• Controlling workflow execution (e.g., retries, looping, escalation)

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Design Invariants

The following invariants must hold in any execution environment:

1. Evidence-bound

   • All key conclusions must be supported by evidence from the knowledge base
   • No external knowledge or common-sense assumptions may be introduced

2. Traceability

   • Every material claim must be traceable to a specific location in the original source (document name, page, section, paragraph, line, or clause)

3. Non-assumptive

   • Insufficient or conflicting evidence must be explicitly marked as uncertain
   • No gap-filling based on plausibility, convention, or experience

4. Conflict-aware

   • Multiple conflicting claims about the same fact must be surfaced with their citations
   • Conflicts are treated as risk signals, not automatically resolved

5. Agentic-compatible

   • Outputs must expose structured signals usable by downstream agents or humans: confidence, sufficient, conflicts, suggested_next_actions

6. Subject Existence (Hard Gate)

   • A conclusion MUST NOT be produced unless at least one candidate subject that explicitly matches the query subject class is identified in the knowledge base
   • If no such subject is found, the system MUST return sufficient = false and MUST NOT substitute semantically similar subjects
   • "I don't know" is a valid capability in agent systems; "hard answering" under subject absence is a defect
   • This invariant prevents semantic fallback under subject absence
   • Applies universally to all subject types: entities, individuals, products, locations, time periods, jurisdictions, etc.

7. Entity Binding

   • A candidate subject MUST have an explicit and verifiable binding to the parent entity specified in the query subject class
   • Role or title similarity alone is insufficient
   • Valid binding mechanisms: ownership, registration, contractual role, or explicit statement in source text
   • If the parent entity relationship cannot be explicitly established from the knowledge base, the subject MUST NOT be considered a valid candidate
   • This invariant prevents accepting subjects that "look like" matches but lack the required entity relationship

8. Subject-Evidence Integrity

   • Any evidence used to support a conclusion MUST explicitly confirm that the subject of the evidence matches the subject of the query
   • If subject identity or scope cannot be unambiguously established, the evidence MUST NOT be used to support the conclusion
   • Subject drift is a critical error that invalidates the entire conclusion
   • This invariant applies universally: companies, individuals, products, locations, time periods, jurisdictions, legal entities, or any other subject type

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When to Use

Use this skill when the task requires:

• Knowledge base Q&A - Answering questions based on specific documents or knowledge bases with source attribution
• Evidence verification - Retrieving and verifying factual claims from documents
• Due diligence queries - Investigating facts from multiple sources with conflict detection
• Multi-source analysis - Synthesizing information from multiple documents with explicit citations
• Fact-checking - Verifying claims against source documents

Typical use cases include:

• Knowledge base queries requiring evidence traceability
• Document analysis with source attribution
• Compliance or legal document review
• Research analysis and fact verification

Not suitable for:

• Casual or conversational generation tasks
• Queries that do not require source attribution or traceability
• Creative writing or brainstorming tasks

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Inputs & Outputs

This skill does not impose a fixed schema on inputs or outputs.

• Inputs are organized by the calling Agent or runtime (e.g., question, knowledge base scope, constraints).
• Outputs aim to be structured and machine-consumable, but concrete field shapes are determined by the integration context.

Core invariants enforced by the skill:

• All conclusions must be evidence-backed
• Conflicts, uncertainty, and risk signals must be explicitly surfaced

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Execution State Machine

This skill implements a strict, irreversible state machine to eliminate non-deterministic execution paths.

States

stateDiagram-v2
    [*] --> INIT
    INIT --> SUBJECT_CLASS_IDENTIFIED
    SUBJECT_CLASS_IDENTIFIED --> CANDIDATE_SUBJECT_DISCOVERED
    CANDIDATE_SUBJECT_DISCOVERED --> SUBJECT_NOT_FOUND: No candidates
    CANDIDATE_SUBJECT_DISCOVERED --> EVIDENCE_RETRIEVAL: Candidates found
    SUBJECT_NOT_FOUND --> [*]: TERMINATE
    EVIDENCE_RETRIEVAL --> EVIDENCE_VERIFIED
    EVIDENCE_VERIFIED --> CONCLUDED
    CONCLUDED --> [*]: TERMINATE

State Definitions:

State	Description	Terminal
INIT	Initial state, query received	No
SUBJECT_CLASS_IDENTIFIED	Query subject class extracted and validated	No
CANDIDATE_SUBJECT_DISCOVERED	Candidate subjects searched in KB	No
SUBJECT_NOT_FOUND	No matching candidates exist	Yes
EVIDENCE_RETRIEVAL	Knowledge retrieval executed	No
EVIDENCE_VERIFIED	Subject-evidence integrity verified	No
CONCLUDED	Final conclusion generated	Yes

State Transition Rules (MANDATORY)

1. Linearity: State transitions MUST follow the defined order. No state may be skipped.

2. Irreversibility: Once a state is entered, the system MUST NOT transition back to a previous state.

3. Hard Gate Enforcement:

   • CANDIDATE_SUBJECT_DISCOVERED is a HARD GATE
   • If candidate_subjects == [], MUST transition to SUBJECT_NOT_FOUND
   • SUBJECT_NOT_FOUND is a TERMINAL state - no further execution allowed

4. Forbidden Transitions:

   • SUBJECT_NOT_FOUND → any state other than termination
   • Any state → SUBJECT_CLASS_IDENTIFIED (re-planning forbidden)
   • EVIDENCE_RETRIEVAL → SUBJECT_CLASS_IDENTIFIED (query re-interpretation forbidden)

5. Forbidden Behaviors:

   • Skipping CANDIDATE_SUBJECT_DISCOVERED to proceed directly to retrieval
   • Relaxing subject constraints after SUBJECT_NOT_FOUND
   • Substituting semantically similar subjects when candidates are empty
   • Re-interpreting the query to improve answer completeness

Execution Contract

Determinism Guarantee: The same input MUST always produce the same execution path through the state machine.

Termination Guarantee: Execution MUST terminate in one of two terminal states:

• SUBJECT_NOT_FOUND (when no candidate subjects exist)
• CONCLUDED (when evidence-based conclusion is generated)

No-Replay Guarantee: If execution terminates in SUBJECT_NOT_FOUND, the system MUST NOT:

• Retry with alternative search strategies
• Reformulate the query
• Relax subject constraints
• Attempt additional retrievals

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Execution Semantics

State 0: INIT

Receive and validate the query.

State 1: Identify Query Subject Class

Entry Condition: State = INIT

Action:

• Analyze the query to identify the subject class and its defining characteristics
• Extract subject attributes: type, scope, constraints, relationships
• Example: "Entity A contractual obligations" → subject_class = {type: legal_entity, name: Entity A, document_type: contract}

Exit Condition: Subject class successfully extracted

Next State: SUBJECT_CLASS_IDENTIFIED → CANDIDATE_SUBJECT_DISCOVERED

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State 2: Candidate Subject Discovery (HARD GATE)

Entry Condition: State = SUBJECT_CLASS_IDENTIFIED

Action:

• Search the knowledge base for subjects that match the query subject class

• Apply Candidate Subject Validity Rule (MANDATORY):

  A subject S is a valid candidate IFF all conditions are satisfied:

  1. S matches the subject type (e.g., individual, entity, product)
  2. S matches the subject role (e.g., founder, shareholder, contractor)
  3. S has an explicit parent_entity binding that matches the query
     • Via ownership (e.g., "founder of Entity A")
     • Via registration (e.g., "registered agent for Entity A")
     • Via contractual role (e.g., "party to Master Services Agreement with Entity A")
     • Via explicit statement in source text (e.g., "Entity A's founder is...")

  If condition (3) is NOT satisfied, S MUST be rejected.

• INVALID Examples (role similarity without entity binding):

  • Query: "Entity A's founder" → KB has "Company founder" but no binding to Entity A → REJECT
  • Query: "Entity A's contractual obligations" → KB has "MSA obligations" but Entity A is not a party → REJECT
  • Query: "Supplier X's subsidiaries" → KB has "subsidiaries" but not under Supplier X → REJECT

• Output format: candidate_subjects: [{subject_id, name, class_attributes, parent_entity_binding, source_location}]

Decision Point:

IF candidate_subjects == []:

• Set sufficient = false
• Set confidence = low
• Set risk_signal = subject_not_found
• Provide suggested_next_actions for sourcing missing subject information
• Next State: SUBJECT_NOT_FOUND → TERMINATE
• FORBIDDEN: Any further retrieval, query reformulation, or alternative interpretation

IF candidate_subjects != []:

• Proceed to evidence retrieval
• Next State: EVIDENCE_RETRIEVAL

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State 3: Evidence Retrieval

Entry Condition: State = CANDIDATE_SUBJECT_DISCOVERED AND candidate_subjects != []

Action:

• Retrieve candidate document fragments from the specified knowledge base
• CONSTRAINT: All retrieval queries MUST include identifiers from candidate_subjects
• Filter retrieval to ONLY include documents that reference the identified candidate subjects
• Prefer multiple independent sources to enable cross-verification

Exit Condition: Retrieval completed

Next State: EVIDENCE_VERIFIED

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State 4: Evidence Extraction

Entry Condition: State = EVIDENCE_RETRIEVAL

Transform retrieved text into locatable evidence units, ensuring each key claim can be traced back to its original source.

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State 5: Subject-Evidence Integrity Verification

Entry Condition: State = EVIDENCE_EXTRACTION

Action:

• For each evidence unit, verify that the subject of the evidence matches the subject of the query
• Extract and compare subject identifiers: entity names, dates, locations, jurisdictions, roles, scopes
• Reject evidence where subject identity or scope cannot be unambiguously confirmed as matching

Next State: CONCLUDED

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State 6: Concluded

Entry Condition: State = EVIDENCE_VERIFIED

Action:

• Assess confidence based on evidence coverage, independence, and presence of conflicts
• Determine sufficient with respect to the applicable confidence threshold
• When sufficient = false, provide actionable suggested_next_actions
• Generate final conclusion with evidence citations

Next State: TERMINATE

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Failure & Uncertainty Handling

State-Level Failure Handling

SUBJECT_NOT_FOUND State (Terminal):

• Trigger: candidate_subjects == [] at State 2
• Mandatory Actions:
  • Set sufficient = false
  • Set confidence = low
  • Set risk_signal = subject_not_found
  • Provide suggested_next_actions for sourcing missing subject information
• FORBIDDEN Actions:
  • DO NOT proceed to evidence extraction
  • DO NOT proceed to conclusion generation
  • DO NOT retry with alternative search strategies
  • DO NOT reformulate the query
  • DO NOT relax subject constraints
• Execution: MUST terminate immediately

State Machine Invariants

1. No Recovery from SUBJECT_NOT_FOUND: Once in this terminal state, execution MUST NOT continue

2. No Query Reinterpretation: After State 1, the query MUST NOT be reinterpreted

3. No Constraint Relaxation: Subject constraints identified in State 1 MUST NOT be relaxed in subsequent states

4. No Semantic Substitution: Empty candidate_subjects MUST NOT trigger fallback to semantically similar subjects

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Other Failure Modes

• Subject not found (Candidate Subject Gate failure):

  • Set sufficient = false
  • Set confidence = low
  • Set risk_signal = subject_not_found
  • Provide suggested_next_actions for sourcing missing subject information
  • DO NOT proceed to evidence extraction or conclusion generation

• Missing critical evidence: mark sufficient = false and specify missing information

• Conflicting evidence: surface conflicts without arbitration

• Inconclusive results: explicitly state uncertainty and propose verification steps

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Integration Notes

• Downstream Agents or workflows may consume the structured signals to decide:

  • Whether to escalate to human review
  • Whether to request additional materials
  • Whether to trigger further verification or retrieval steps

Note: Workflow control strategies are defined by the calling system. This skill provides only auditable evidence and risk signals.

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Example

The following example illustrates the behavioral characteristics of the skill (evidence grounding, conflict exposure, uncertainty signaling), not a required format.

Example question: Is there a clearly defined constraint or entitlement associated with this matter?

Example output (expressive illustration):

• Conclusion: It cannot be confidently determined whether a clearly applicable entitlement exists.

• Evidence:

  • Policy_Document_v1, Section 3: Mentions a constraint related to the matter, but does not specify scope or activation conditions.

• Conflicts:

  • No direct clause conflicts identified, but descriptions of applicability differ across documents.

• Confidence: Low to medium.

• Risk note: Without clear scope and activation conditions, this conclusion should not be treated as definitive.

• Suggested next actions:

  • Clarify the applicable subjects of the clause
  • Verify whether supplementary or final versions of the document exist

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Example 2: Candidate Subject Gate (Subject Not Found)

Example question: What are the contractual obligations of Entity A under the Master Services Agreement?

Example execution with Subject Existence Invariant:

• Step 1 - Identify Query Subject Class:

  • subject_type: legal entity
  • document_type: Master Services Agreement
  • relationship: contractual obligations

• Step 2 - Candidate Subject Discovery:

  • Search knowledge base for: "Entity A" + "Master Services Agreement"
  • Result: candidate_subjects: []
  • HARD GATE TRIGGERED

• Step 3 - Short-circuit (DO NOT proceed to evidence retrieval):

  • sufficient: false
  • confidence: low
  • risk_signal: subject_not_found
  • Conclusion: The knowledge base does not contain a Master Services Agreement for Entity A.

• Suggested next actions:

  • Request Master Services Agreement for Entity A
  • Verify if Entity A is a party to any similar agreements in the knowledge base
  • Clarify whether Entity A operates under a different legal name

What MUST NOT happen:

• DO NOT retrieve contractual obligations for Entity B as a substitute
• DO NOT assume Entity A's obligations match those of other entities
• DO NOT proceed with evidence extraction when no candidate subjects exist

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Example 3: Entity Binding Invariant (Invalid Candidates Rejected)

Example question: What is the educational background of Entity A's founder?

Example execution WITHOUT Entity Binding Invariant (WRONG):

• Step 1 - Identify Query Subject Class:

  • subject_type: individual
  • role: founder
  • parent_entity: Entity A

• Step 2 - Candidate Subject Discovery (WITHOUT binding validation):

  • Search knowledge base for: "founder" + "education"
  • Found: "John Doe, Founder of [Company], MBA from Harvard"
  • Result: candidate_subjects: ["John Doe"] (WRONG - no Entity A binding)
  • Proceeds to evidence retrieval → INCORRECT CONCLUSION

Example execution WITH Entity Binding Invariant (CORRECT):

• Step 1 - Identify Query Subject Class:

  • subject_type: individual
  • role: founder
  • parent_entity: Entity A

• Step 2 - Candidate Subject Discovery (WITH binding validation):

  • Search knowledge base for: "founder of Entity A"
  • Potential matches found:
    • "John Doe, Founder" → REJECTED (no parent_entity binding to Entity A)
    • "Jane Smith, CEO of Entity A" → REJECTED (wrong role: CEO, not founder)
  • Apply Entity Binding Validity Rule:
    • Condition 1 (type): ✅ John Doe is an individual
    • Condition 2 (role): ✅ John Doe is a founder
    • Condition 3 (parent_entity binding): ❌ No explicit binding to Entity A found
  • Result: candidate_subjects: [] (all candidates rejected)
  • HARD GATE TRIGGERED

• Step 3 - Short-circuit:

  • sufficient: false
  • confidence: low
  • risk_signal: subject_not_found
  • Conclusion: The knowledge base does not contain information about Entity A's founder.

Key Difference:

• WITHOUT binding check: Accepts any "founder" → semantic fallback → wrong answer
• WITH binding check: Requires "Entity A's founder" → correct rejection → "I don't know"