Andrej Karpathy's LLM Wiki shows how an agent can compile a personal knowledge base into cross-linked markdown. But as your collection of files grows, retrieval slows. While a search box lets an agent sample a corpus by similarity, it never shows how that corpus is organized or what it connects to. 

In this technical webinar, we’ll discuss why knowledge bases need a graph. Learn how to model documents, sections, and relationships in Neo4j to turn an LLM wiki into a knowledge layer that AI agents can navigate, not just search. See how GraphRAG outperforms vector-only approaches with 2x better precision and recall, based on an experimental study from the National Innovation Centre for Data. We’ll also demonstrate a live build of deterministic GraphRAG on Neo4j without LLMs or embeddings during ingestion.

This session will help AI teams scale a personal knowledge base to a production-ready knowledge layer. As a result, AI agents can answer complex questions more accurately.