Retrieval Augmented Generation (RAG)

In this module, you will learn:

• What Retrieval Augmented Generation (RAGFetching relevant context from an external data source as additional context to inform a language model's response.) is and how you can use it to improve GenerativeAI model responses.
• How vectors and embeddingsInformation represented as a numerical vector, positioned so that similar information sits close together. 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 AIModels that produce new content rather than classifying or scoring content that already exists. applications.
• About GraphRAGRetrieval-augmented generation whose context comes from a knowledge graph, so the model can follow the relationships between facts. techniques, and how they can be used to enhance information retrieval.

If you are ready, let's get going!