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Designing Provenance Systems for AI Products

7 chapters · 32 lessons

1. 1. Capture Mechanisms for Content Origin

   4 lessons

   Build systems that record source context, device metadata, and model involvement at the moment of content creation or ingestion.

2. 2. Logging Transformation Chains

   5 lessons

   Implement systems that track edits, generations, compressions, and retrieval steps as content moves through your product.

3. 3. Integrity Verification and Tamper Detection

   4 lessons

   Design mechanisms that validate provenance chains and detect when content or metadata has been altered outside recorded transformations.

4. 4. User-Facing Provenance Displays

   5 lessons

   Translate technical provenance metadata into trust signals and decision-relevant cues that users can act on.

5. 5. Provenance in Retrieval-Augmented Generation

   5 lessons

   Build citation and source-tracking systems that distinguish retrieved facts from model-generated inference in RAG pipelines.

6. 6. Provenance Across Edge, Mobile, and Cloud

   4 lessons

   Architect provenance systems that balance on-device logging, cloud auditability, and performance constraints across distributed systems.

7. 7. Provenance for Enterprise and Compliance

   5 lessons

   Design audit-ready provenance systems for knowledge management, legal workflows, and regulated industries.