Retrieval-augmented generation (RAG)
Retrieval-augmented generation (RAG) is a technique that grounds a language model's answer in your own documents: the system retrieves the most relevant passages, then asks the model to answer using them, so the response reflects your content, not just its training.
A model alone can only draw on what it learned during training, which is frozen and generic. RAG fixes that by first searching a knowledge base for passages relevant to the question, then giving those passages to the model as context for its answer.
The payoff is answers that are current, specific to your organization, and traceable: because the model is told which sources to use, the answer can cite them, and it is far less likely to invent facts.
Spojit's knowledge base uses RAG: point it at your docs and an agent answers from them with the sources cited, instead of guessing. See the internal knowledge assistant solution.
See it in a real workflow
Spojit puts these ideas to work: describe what you want and Miraxa builds the workflow. Start free, no credit card required.