A blog about Product Management, Data Management and Self-Driven Learning
Effective AI governance hinges on explicit oversight modes, including Human-in-the-loop (HITL), Human-on-the-loop (HOTL), and automated systems. Each mode serves distinct use cases based on impact level. Proper documentation, data acquisition, and structured workflows are essential to ensure accountability and transparency, moving beyond vague assurances of human involvement.
RAG systems rely on up-to-date content for accurate responses. Regular content updates, deletions, and re-indexing are crucial to avoid referencing obsolete information. Governance requires managing personal data removal and sensitive content. Effective archiving and versioning support knowledge management, ensuring the assistant reflects current information and policy changes.
The governance of Retrieval-Augmented Generation (RAG) assistants focuses on access control and risk management during the "Use" stage. Key risks include access leakage, over-general answers, adversarial queries, and misleading confidence. Implementing role-aware retrieval, constraining query types, ensuring transparency in answers, and monitoring usage patterns are essential for effective governance.
The content discusses the importance of concrete metrics in AI governance using the DASUD framework. Key principles include measuring behaviour, focusing on leading indicators, and aligning metrics with business goals. It outlines specific metrics for the Design, Acquire, Store, Use, and Delete stages of AI systems, emphasising systematic governance throughout the AI lifecycle.
The content discusses the importance of knowledge governance in designing a RAG (Retrieval-Augmented Generation) assistant, focusing on two main aspects: acquisition of content and its storage. It emphasises selecting appropriate sources, cleaning and tagging content, and ensuring effective document management over time, including version control and retention policies.
Nigel D'Souza 