KEG vs RAG - Why Knowledge-Engineered Generation is the Future of Augmented Language Models

RAG is only as good as the text in the store it retrieves from, and what’s retrieved is only useful as context.

KEG (Knowledge-Engineered Generation), on the other hand, enforces a much higher quality of reference context, its concepts can be used for governance, and it allows for more “human in the loop”.

See the article at Beyond RAG: Knowledge-Engineered Generation for LLMs

Great article but I have so many questions, like how do you get extra HITL using a KG?

For chat (say) The simplest way is to introduce a browsing function into the chat UI. The user can then explore the concepts and then choose one to ask a question about.

You can try it out by clicking up the beth chat icon to the right and typing “knowledge graph”. Browse by entering a number.