A blog about Product Management, Data Management and Self-Driven Learning
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.
The effective use of AI agents requires controlled action capabilities like creating tickets or sending emails, which must be governed to prevent misuse. A capability catalogue should detail each tool's function, risk level, and necessary oversight. Implementing strict policies and logging actions ensures safety, compliance, and accountability.
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.
The "Acquire" stage for AI agents focuses on managing tools and data access rather than just training data, emphasising risk assessment for each capability. Proper classification of tools by risk ensures controlled access. Effective governance includes defining roles, filtering data, and validating feedback to prevent misuse, outlined in a governance playbook.
Nigel D'Souza 