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.