Beyond RAG: Building Reliable AI Systems for Privacy Assessments
summary
Ten minutes of practical engineering: replaces plain RAG with tool use / function calling to call APIs and avoid hallucinations, a claim-evaluation loop that runs model outputs against source text (claim extraction), and structured outputs via a lightweight markup called BAML output normalization so booleans and variables are machine-readable. Emphasizes evidence weighting (code = high trust, vendor questionnaires = low), prefers inference-time injection over costly fine-tuning, and reports these patterns succeed in reducing hallucinations in real deployments.
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chapters (8)
- 0:00AI for privacy systems
- 1:12When to use AI for privacy
- 3:39What is RAG
- 5:05Beyond RAG with tools
- 8:12Normalizing AI outputs
- 10:01Fine-tuning as last resort
- 11:25AI privacy assessment challenges
- 13:07Future of AI and privacy
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