Victor A

AI Response Consensus & Trust Score Implementation Plan

AI Response Consensus & Trust Score Implementation Plan

Phase 1: Core Consensus Engine

  1. Create ConsensusService - Calculate trust scores and detect consensus across AI responses
  2. Enhance ResponseMetrics - Add trust scoring, confidence levels, and consensus markers
  3. Implement Hallucination Detection - Cross-validate responses to identify potential inaccuracies

Phase 2: Trust Scoring Algorithm

  1. Content Similarity Analysis - Measure agreement between models on factual content
  2. Source Verification - For models with grounding (Gemini), use external verification
  3. Confidence Weighting - Weight responses based on model confidence and historical accuracy
  4. Consensus Calculation - Generate final consensus with trust percentage

Phase 3: UI Integration

  1. Trust Score Display - Visual indicators showing consensus strength (0-100%)
  2. Consensus Highlighting - Mark agreed-upon content vs. disputed sections
  3. Confidence Indicators - Color-coded trust levels for different response sections

Phase 4: Gemini Integration Enhancement

  1. Leverage Gemini for Analysis - Use Gemini's grounding for fact-checking other responses
  2. Cross-Model Validation - Compare grounded vs. non-grounded responses for accuracy
  3. Truth Consensus - Generate final "truth score" based on multiple validation layers