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Most teams don't need to train their own model. They need an assistant that answers questions from their own documents, tickets or database, accurately and with sources. That is what retrieval-augmented generation, or RAG, is for. ## What RAG is, in one paragraph When a user asks a question, the system first searches your content for the most relevant passages, then gives those passages to a large language model and asks it to answer using only that material. The model writes the answer; your data supplies the facts. ## Good first use cases - Customer support: answer common questions from your help centre and past tickets. - Internal knowledge: let staff search policies, specs and runbooks in plain English. - Sales: draft proposal sections from previous proposals and case studies. - Operations: explain order, shipment or account status from your own systems. Pick one use case with a clear owner and a measurable outcome, such as fewer support tickets or faster answers. ## The building blocks - Ingestion: collect documents, split them into small chunks and keep the source link for each. - Embeddings and a vector index: turn each chunk into numbers so similar meaning can be found quickly. - Retrieval: for each question, fetch the best few chunks, combining semantic and keyword search. - Generation: send the question and chunks to the model with clear instructions to cite sources and say "I don't know". - Guardrails: permission checks so users only see answers from content they are allowed to read. ## Measure before you launch Write 50 to 100 real questions with known good answers. Run them after every change and track how many answers are correct, grounded in the sources and complete. This test set is the single most important part of the project. ## Keep costs predictable - Cache answers to frequent questions. - Use a smaller, cheaper model for simple questions and a larger one only when needed. - Limit how many chunks are sent per question. - Monitor cost per conversation from day one, not after the first invoice. ## A realistic timeline A focused first version, covering one use case, one content source, an evaluation set and a simple chat interface, typically takes a few weeks. Connecting more sources, adding permissions and integrating with your product comes next, in small steps. Thinking about AI for your product? Book a free feasibility call and we will tell you honestly what will work, what it will cost and what to skip.

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