On-premise RAG for internal documents
A model can only answer questions about your documents if it has read them, and reading them means either sending them to a third party or keeping the whole pipeline on infrastructure you control. For internal documents, that second option isn't a preference, it's the only one that holds up.
What RAG actually does
Retrieval-augmented generation is a workaround for a real limitation: a language model only knows what was in its training data, and your contracts, HR records, and internal source code were not. RAG closes that gap by splitting your documents into chunks, converting each chunk into a vector with an embedding model, and storing those vectors in a database. When someone asks a question, the system embeds the question the same way, finds the chunks whose vectors are closest to it, and hands those chunks to the language model as context alongside the question. The model then answers using text it was just given, rather than text it memorized during training.
The upside is that the model can be current and specific without retraining. Add a new contract, it's searchable within minutes. The tradeoff is that every document in that pipeline passes through an embedding step and sits in a vector database somewhere, and both of those are places data can leak if you don't control where they run.
Why the "internal" documents are the ones that matter
Nobody worries about RAG over a public product manual. The documents worth building RAG for are usually the ones an organization can't show outsiders: signed contracts with pricing and liability terms, HR records with salary and disciplinary history, unreleased source code, cap tables, customer data under a data-processing agreement that doesn't list your AI vendor as a subprocessor. Feeding those into a cloud RAG service means the vector database, the embedding calls, and the retrieved-context prompts sent to the model all pass through a third party's infrastructure, under whatever retention and training terms that vendor's specific plan has on the day you signed up.
Running the same pipeline on-premise or on dedicated hardware removes that exposure by construction. The embedding model, the vector store, and the language model all run on a box your organization controls. Documents never leave the network to get chunked, embedded, or answered from. That's the difference between "our vendor says they don't train on this" and "this data was never transmitted to begin with," and for anything covered by an NDA, attorney-client privilege, or employment law, that difference is the whole point.
What it takes to run this yourself
Open WebUI is a practical way to stand this up without building a retrieval pipeline from scratch: it runs an embedding model locally alongside your main model, lets you upload documents through the admin panel, and handles the chunk-embed-retrieve loop internally. The setup notes two things worth knowing before loading a large corpus. The default embedding model is tuned for size rather than retrieval quality, so swapping it for a stronger multilingual embedder is usually the single biggest quality improvement available. And retrieval quality depends far more on how documents are split than on which model answers the question, a PDF that's mostly tables will disappoint regardless of what's serving the response.
The hardware side is the same DGX Spark that runs any of GPUwerk's models: 128 GB of unified memory is enough to hold a mid-sized language model, an embedding model, and a vector index for a reasonably large document set at once, without needing a second machine. For the broader tradeoffs of running this kind of infrastructure yourself versus renting it, see our guide to private LLM hosting.
What RAG doesn't fix
RAG improves what the model can answer about, it doesn't improve how well it reasons, and it introduces a new failure mode of its own: retrieving the wrong chunk and answering confidently from it. Neither of those is a privacy problem, but both are worth knowing before treating RAG as a solved problem rather than a pipeline that needs the same tuning and testing as any other piece of infrastructure.