Retrieval Augmented Generation (RAG)

In this module, you will learn:

• What Retrieval Augmented Generation (RAG) is and how you can use it to improve GenerativeAI model responses.
• How vectors and embeddings work, and how they can be used in RAG to find relevant information.
• How to use a vector indexes in Neo4j and when they are useful for finding context for Generative AI applications.
• About GraphRAG techniques, and how they can be used to enhance information retrieval.

If you are ready, let's get going!