What it is
RAG combines search and generation: first it retrieves the most relevant pieces of your documents (retrieval), and then the model generates the answer based on them (generation). That way it answers with your data, not only with what it learned in training.
Why it matters to an agency
You can give an agent your business knowledge (manuals, policies, catalogs) without paying for fine-tuning. You update the knowledge by changing the documents, not the model.
Example
A support chatbot that answers by citing the client's manual: it finds the right passage and uses it to reply accurately.
By the numbers
A typical RAG pipeline splits documents into chunks of 500 to 1000 tokens and retrieves the 3 to 5 most relevant ones per query. Unlike fine-tuning, it retrains nothing: updating knowledge means reindexing a document, a matter of minutes, while retraining a model takes hours and budget.