This video focuses on maintaining and validating a knowledge base for Retrieval-Augmented Generation (RAG) to ensure long-term accuracy, relevance, and performance. A robust maintenance process is critical for AI agents to consistently retrieve the correct information.

Keeping a knowledge base up to date involves regularly replacing outdated files with updated versions. Automating this process with Botpress’s File API can streamline updates and reduce the risk of outdated content affecting responses. Conducting regular audits helps identify and remove irrelevant or obsolete information, keeping the dataset focused and effective.

Validation is an essential step after uploading new content. Running test queries against the knowledge base ensures the agent retrieves and delivers accurate, contextually relevant responses. Utilizing tools such as event debuggers, production logs, and emulators provides insights into how the agent prioritizes information. Feedback from internal or external users can highlight areas for improvement, guiding adjustments to document structure or content.

Consistent version control, audits, and feedback loops create a well-maintained knowledge base capable of supporting accurate and reliable AI-driven interactions.

You can learn more about Botpress at botpress.com/