LLM + Graph Database for Retrieval Augmented Generation (RAG)

LLMs are often like the know-it-all at a bar – they can quickly and confidently produce realistic sounding answers to just about any question – even if the answers are complete fabrications. But an LLM can be grounded in reality by combining it with a Knowledge Graph in order to prevent hallucinations, and to prevent unauthorized access to sensitive data.
This presentation will show you the benefits of Graph Databases over regular databases and how to use GenAI with RAG to eliminate hallucinations, enforce security, and improve accuracy. We will also discuss why a vector index plus Knowledge graph provides better, smarter, faster results than a pure vector database.
We will demonstrate an end-to-end retrieval pipeline. The code in the demo will be available in a Jupyter notebook on Github for you to reuse.

SPEAKER BIO
Soham Dhodapkar, is a Solution Engineer at Neo4j, helping users all over the world solve complex problems using the power of Graph Databases. He is an AI and Data Science enthusiast with research experience in Machine Learning, Analytics and Natural Language Processing. Soham is a conversationalist, likes stargazing, hiking and is a recreational tennis player.