RAG (Retrieval-Augmented Generation) is a method where an AI language model does not generate its answer freely from memory but first retrieves matching content from its own knowledge source and uses that as the basis. The model combines retrieval (finding the relevant passages) with generation (phrasing the answer). The result: verifiable answers from your own data with far fewer hallucinations.
Your content is split into small chunks and stored as vectors (embeddings) in a database. For a question, the system finds the semantically closest chunks and passes them to the model together with the question.
The model then answers based on exactly those sources — often with a citation. It doesn't guess; it phrases from what it was given.
A language model alone doesn't know your internal content and can invent facts. RAG brings in current, company-owned knowledge without having to retrain the model.
That is cheaper, more current and traceable: you change the knowledge source and the answers change with it — without expensive re-training.
Support chatbots based on your own documentation, internal knowledge assistants, product advisors in the shop. Anywhere answers must be correct, current and from defined sources.
Our chat product SilentChat uses RAG to answer from your own content instead of generic model knowledge.
Yes, significantly — because the model answers from supplied, real sources instead of guessing freely. Errors can't be ruled out entirely; good sources and citations help further.
No. That's the advantage: RAG brings in your knowledge at runtime. You simply update the knowledge source without retraining the model.
Fine-tuning changes the model itself with training data. RAG leaves the model unchanged and supplies knowledge at answer time — more flexible when content changes.