Building Better GenAI: Your Intro to RAG & Graphs


Retrieval-augmented generation (RAG) and graphs are a game-changing duo for anyone who wants accurate, explainable, and context-aware GenAI responses.

Pre-trained large language models (LLMs) like ChatGPT are incredibly powerful, but their business applications are limited when used as a standalone tool. Why? Because they are rooted in general, publicly available data that can quickly become outdated.

By combining RAG and graphs, developers can enhance pre-trained LLMs with relevant, business-specific data from a vast knowledge base. 

This on-demand webinar is a 30-minute primer on these two technologies. During the session, you’ll explore:
  • The basics of RAG
  • Why and how graph databases improve RAG, and how this synergy enhances GenAI
  • How to use Neo4j’s vector search, seamless integrations, and data science tools to improve results

Once you understand how RAG and graphs work together, you’ll be ready to use RAG to its full potential. 


Zachary Blumenfeld Image

Zachary Blumenfeld
Product Specialist

Zach helps empower the field with Neo4j’s industry-leading graph data science capabilities. Zach is a graph enthusiast who helps data scientists, engineers, and business leaders understand and implement graph analytics to solve challenging business problems. He has first-hand experience with a wide range of modern-day analytical challenges, including criminal fraud detection, identity resolution, and recommendation systems. Serving in both data science and software developer capacities, Zach has applied graph computing to law enforcement and government entities in support of missions that counter drug trafficking, human smuggling, money laundering, and child exploitation. He has led the development and deployment of full-stack graph systems designed to facilitate broad search and analytical query requirements.

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