RAG (Retrieval-Augmented Generation)

Retrieval-augmented generation (RAG) is a technique that lets a large language model pull in relevant, up-to-date information from an external data source at the moment a question is asked, instead of relying solely on the knowledge baked into its weights during training. The retrieved passages are inserted into the prompt, so the model's answer is grounded in real documents rather than generated from memory alone.

How It Works

A RAG pipeline first splits a knowledge base into chunks and indexes them — typically as embeddings in a vector database that supports semantic similarity search. When a query arrives, the system retrieves the most relevant chunks, appends them to the prompt, and the LLM generates an answer using that supplied context. This grounding step is what reduces hallucination and lets a model cite current or proprietary information it was never trained on.

Why It's Powerful for Sovereign AI

RAG lets you bolt your own private documents — notes, manuals, archives — onto a local model without retraining it, keeping sensitive data on your own hardware. A self-hosted model plus a local vector store gives you an answer engine over your own knowledge that never phones home. It also sidesteps context window limits by retrieving only the passages that matter for each query.

RAG complements local inference and is a cornerstone of self-hosting private AI assistants.