AI reliability and evaluation
Hallucination Audit System
Breaks generated catalog content into claims and labels each against source context: Exact Match → No Match.
Problem
Content generation quality reviews lacked granularity and could not separate grounded claims from risky invention.
Solution
Designed information-unit extraction and support taxonomy: Exact Match, Paraphrase, Derivation, Extrapolation, No Match.
Outcome
Enabled reliability dashboards that informed prompt tuning, policy thresholds, and human review prioritization.
Architecture
A placeholder implementation path that can be expanded with screenshots, data contracts, system diagrams, and measurable results as the project matures.
01
Generated text input
02
Claim extraction
03
Context alignment
04
Evidence labeling
05
Risk scoring
06
Audit report
Product Artifacts
Sanitized examples to demonstrate product thinking and execution style when proprietary materials cannot be shared.
- PRD outline (problem framing, success metrics, rollout plan)
- Workflow wireframe / journey snapshot
- Evaluation rubric or quality checklist
- Operational metrics dashboard mock
Metrics to Track
- Unsupported claim rate
- Extrapolation share
- No Match severity
- Prompt iteration delta
Product Role
- Designed taxonomy and labeling logic
- Defined reviewer-facing outputs
- Connected results to iteration loop