Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a technique that enhances AI-generated responses by first retrieving relevant information before generating an answer. To break it down, generation refers to how LLMs create responses, while retrieval is the search process that supplies the model with contextually relevant data before it generates a response. This retrieval step, often powered by semantic search and embeddings, helps the AI stay accurate, up-to-date, and contextual by fetching relevant documents or data before responding.

More advanced versions, like graph-based RAG, focus on retrieving connections between pieces of information, rather than just individual documents. This allows AI to map relationships and understand how different concepts link together. My mental model for RAG is like a customer support call—before answering your question, an agent asks for your name, address, and customer reference number to pull up the right records. RAG does the same for AI systems, retrieving relevant data before generating a response, making it one of the best ways to improve accuracy and relevance in LLMs.