A blog about Product Management, Data Management and Self-Driven Learning
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.
A Retrieval-Augmented Generation (RAG) assistant enhances assistance by combining a language model with a document retrieval layer. Effective design focuses on defining its mission, selecting appropriate knowledge domains, classifying content, and addressing uncertainty. A clear design sheet guides the process, ensuring responsible knowledge management and user support in various contexts.
Adding memory to AI agents enhances their helpfulness but introduces governance challenges regarding what data is stored, its duration, visibility, and potential leaks. Effective memory management involves segmenting data by scope and sensitivity, establishing storage rules, enabling user control, limiting access, and ensuring proper reset mechanisms to mitigate risks of data misuse.
Nigel D'Souza 