What is retrieval-augmented generation?
Retrieval-augmented generation, or RAG, is a method where an AI system first searches your own documents for relevant passages and then uses those passages to write its answer, so responses are grounded in your material rather than the model's memory.
A language model on its own answers from what it absorbed in training, which knows nothing about your price book, your SOPs or last month's policy change. RAG adds a lookup step. When someone asks a question, the system searches an index of your documents, pulls the few passages that match best, and hands them to the model with an instruction to answer from those.
This is how most internal knowledge assistants work. It reduces made-up answers, lets the tool cite the source page, and means you update the knowledge by updating the documents, not by retraining anything. Quality depends heavily on the documents themselves: outdated, duplicated or contradictory files produce confused answers no matter how good the model is.
Before a RAG project, clean up the source folder. Decide which documents are authoritative, archive the old versions, and set an owner for keeping them current. Also check permissions, so a front-desk assistant cannot surface HR files or partner compensation memos. Measure answer accuracy on a set of real questions your staff ask every week.
Example
For example, an HVAC company could load its equipment manuals, warranty terms and internal troubleshooting notes into a RAG assistant for technicians. A tech in a basement asks why a specific furnace model is throwing a fault code, and the assistant answers with the relevant manual page attached. A service manager reviews a sample of answers each week for accuracy.