Vector Databases and RAG

This Pathway examines the role of vector embeddings and vector stores, offering a detailed understanding of the Retrieval‑Augmented Generation (RAG) process. It explains how RAG enables Large Language Models (LLMs) to ground their responses using external, factual data.

After completing this Pathway, you will be able to:

• Identify the components of a RAG pipeline and explain how external data is retrieved and integrated into an LLM-generated response
• Design and manage vector embeddings to efficiently represent and organise unstructured data