RAG (Retrieval-Augmented Generation), explained
RAG, or retrieval-augmented generation, is a technique where an AI first retrieves relevant documents and then answers using them, so its response is grounded in real sources instead of memory alone.
By default, a language model answers from patterns it learned during training, which means it cannot know your company's documents and may be out of date. RAG fixes that by adding a step: before answering, the system searches a collection of documents you provide, pulls the relevant pieces, and hands them to the model to answer from.
The payoff is answers grounded in your actual content, with less hallucination and the ability to cite where something came from. It is how tools answer questions about your own files, a knowledge base, or recent information the model was never trained on.
You do not need to build RAG to benefit from the idea. Even pasting the relevant document into a chat is a manual version of the same principle: give the model the source, and it stops guessing.
Go deeper
Wield's AI Foundations track covers this hands-on, in plain English, with real examples and a copy-paste prompt to try it yourself.
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