Q: What is RAG, in plain terms?

RAG (Retrieval-Augmented Generation) means giving an LLM the right reference material at the moment you ask it a question.

LLMs know what they learned in training. They don't know your company's documents, your product specs, or anything written after their training cutoff. RAG fixes this: before the model answers, your system searches a knowledge base for the most relevant passages and injects them into the prompt. The model answers from that material instead of guessing.

The pipeline: split your documents into chunks, convert each chunk into an embedding (a numerical representation of meaning), store those in a vector database, and at question time retrieve the chunks closest in meaning to the query.

RAG is the workhorse of applied AI. Most real business use cases (support bots, internal knowledge assistants, document Q&A) are RAG systems at their core.