Everything about Retrieval Augmented Generation (RAG) Course

RAG is a technique that allows AI models to search for relevant information before generating an answer. Instead of relying only on memory, the model responds using trusted external knowledge sources.

Embeddings convert text into dense numerical vectors that capture meaning, context, and relationships between words or sentences. In RAG, these vectors help the system find the most relevant information by comparing semantic similarity instead of exact keyword matches.

Vector similarity measures how close two embeddings are in vector space, helping us identify semantically related content. In RAG, it is used to retrieve the most relevant documents by comparing the user query embedding with stored document embeddings.