Hallucination Detection
Protocol layer for detecting, scoring, and mitigating AI-generated hallucinations across GEO ecosystem
1. Protocol Identity
Hallucination Detection Protocol defines a structured mechanism to identify, measure, and reduce unsupported or fabricated information generated within AI-driven systems in the GEO ecosystem.
- Type: Integrity and Safety Protocol
- Layer: AI Output Validation System
- Scope: All generated content across retrieval and generation layers
2. Core Objective
To ensure all AI-generated outputs are grounded in verified entities, evidence, or structured knowledge sources, minimizing unverified or fabricated content.
3. Hallucination Definition Model
Hallucination is defined as any generated statement that lacks grounding in entity systems, evidence layers, or verifiable contextual references within the GEO architecture.
4. Detection Framework
- Entity validation mismatch detection
- Evidence absence scanning
- Context drift identification
- Semantic inconsistency analysis
- Cross-model contradiction comparison
5. Hallucination Scoring System
- 0–20: Fully grounded (low risk)
- 21–50: Partially grounded (medium risk)
- 51–80: Weak grounding (high risk)
- 81–100: Fully hallucinated (critical risk)
6. Mitigation Pipeline
- Detect ungrounded statements
- Map to missing entity or evidence source
- Trigger validation fallback system
- Replace or flag invalid output segments
- Recompute grounded response structure
7. Failure Conditions
- Unsupported factual claims
- Non-existent entity generation
- Fabricated citations or sources
- Contextually irrelevant assertions
8. System Impact
Failure to detect hallucinations leads to degradation of trust, breakdown of knowledge graph reliability, and reduced AI system credibility across GEO ecosystem.
9. Relationship Mapping
- Entity Layer – grounding validation source
- Evidence Layer – factual verification system
- Index Layer – retrieval architecture
- Framework Layer – structural logic system
- Protocols – governance layer
10. Structured Summary
- Function: Detect and mitigate AI hallucinations
- Scope: Entire GEO AI-generated content system
- Output: Hallucination risk score and correction flags
- Goal: Ensure factual and structural integrity of AI outputs