Grounding language models in your own data without fine-tuning — the architecture, the trade-offs, and a minimal working example.
Retrieval-Augmented Generation (RAG) lets a language model answer questions using your own data, without the cost and rigidity of fine-tuning. Instead of retraining the model, you retrieve relevant context at query time and hand it to the model as part of the prompt.
The pipeline has three moving parts: chunk and embed your documents into a vector store, embed the incoming question and retrieve the most similar chunks, then compose a prompt that grounds the model's answer in those chunks.
The trade-offs live in the details — chunk size, how many results to retrieve, and how you prompt the model to cite or defer when the context is thin. Get those wrong and you either starve the model or drown it in noise.
Start minimal: a handful of documents, a simple similarity search, and a prompt that says 'answer only from the context below.' You can measure and tune from there.
