Customers and Contact Center agents can read and understand knowledge in the format their current format. The context and nuance within the data can be infered by a person and plays a significant role in understanding how to apply the data.

Large Language Models (LLMs) don't magically infer the context that your customers and Contact Center agents can, and they also lack the ability to resolve conflicting statements or identify outdated, irrelevant content that might still exist in legacy environments.

This solution provides a pattern for integrating with any data source, extracting relevant content and splitting it into blocks, enriching it with semantic meaning then vectorizing that data into useful embeddings, scoring content based on quality and accuracy, connecting data to establish concepts and linkages across content types, and implement a RLAIF monitoring and performance management loop.