Overview
This technical report provides a comprehensive evaluation of techniques for making AI outputs trustworthy in high-stakes domains — legal, financial, regulatory, and clinical. It covers hallucination mitigation strategies, multi-agent architectures, citation verification methodologies, and infrastructure patterns for verifiable AI outputs.
Techniques evaluated
- Evaluator-optimizer loops — Independent verification agents cross-checking outputs against source documents
- Context isolation — Enforcing strict context boundaries between specialist agents
- Citation graph mapping — Building structured maps of legal precedent relationships
- Confidence scoring — Per-extraction confidence scores with uncertainty-aware downstream processing
- Cryptographic provenance — IETF VAP/LAP audit trails for AI-generated legal content
Applicability
Each technique is evaluated against three dimensions: accuracy improvement, latency overhead, and implementation complexity. The report provides decision frameworks for teams deploying AI in high-stakes domains, helping them choose which techniques to adopt based on their specific use case requirements.