Build a private knowledge assistant with RAG

Imagine asking a question and getting an answer that cites the exact paragraph from your internal policy, your product specification, or last quarter's audit report — all without a single byte leaving your server room. That is the promise of Retrieval-Augmented Generation (RAG), and with Privonis running entirely on-premise, it is now within reach for any European company that takes data sovereignty seriously.

What is RAG and why does it matter?

Large language models are powerful reasoners, but they only know what they were trained on. RAG fixes that by fetching relevant passages from your own document store at query time and handing them to the model as context. The model then answers grounded in those passages, quoting sources rather than hallucinating facts. The result is a knowledge assistant that is both accurate and auditable — two properties that matter enormously in regulated industries.

The RAG pipeline step by step

A production RAG system involves six stages. Understanding each one helps you avoid the most common failure modes.

• Ingest: load documents from PDFs, Word files, Confluence pages, SharePoint, or any structured source your organisation uses.
• Chunk: split documents into segments — typically 200–500 tokens — that are small enough to fit in the model context window but large enough to carry meaning.
• Embed: convert each chunk into a dense vector using a local embedding model such as BGE-M3 or E5-multilingual. No cloud call required.
• Vector index: store embeddings in a vector database (Qdrant, Chroma, pgvector) running on your own infrastructure.
• Retrieve: at query time, embed the user question and find the top-k nearest chunks by cosine similarity, optionally combined with BM25 keyword search (hybrid retrieval).
• Generate: pass the retrieved chunks plus the question to your on-premise LLM (Llama 3, Mistral, Qwen or another open-weight model served via Ollama or vLLM) and produce a cited answer.

Keeping it private with Privonis

Every step of this pipeline runs inside your infrastructure when you deploy with Privonis. The embedding model, the vector database, the LLM inference server and the orchestration layer are all self-hosted. Your documents never leave your network. This is not just a privacy preference — for companies subject to GDPR, the NIS2 directive, or sector-specific rules in finance and healthcare, keeping data on-premise is often a compliance requirement, not an option.

Chunking and retrieval quality tips

The quality of your RAG system lives or dies at the chunking and retrieval stages. A few practices that consistently improve results: use semantic chunking rather than fixed token counts where possible; overlap chunks by 10–15% to avoid cutting context at boundaries; store document metadata (source, date, section heading) alongside each chunk so the model can cite accurately; and experiment with re-ranking the retrieved passages with a cross-encoder model before sending them to the generator.

The answer is only as good as the retrieval. Invest in chunking strategy and hybrid search before you invest in a bigger model.

Evaluating your knowledge assistant

Evaluation is often skipped in early RAG projects and regretted later. Build a golden dataset of 50–100 question-answer pairs from domain experts. Measure retrieval recall (did the right chunk appear in the top-k results?), answer faithfulness (does the answer stick to what the retrieved text says?) and answer relevance (does it actually address the question?). Open-source frameworks such as RAGAS or DeepEval can automate much of this scoring and integrate into a CI pipeline so regressions are caught before deployment.

Common pitfalls to avoid

The most frequent mistakes we see when helping companies build knowledge assistants: embedding low-quality or duplicate documents without cleaning them first; choosing a chunk size that is too large, causing the model to miss the specific sentence that answers the question; ignoring multilingual documents (BGE-M3 and E5-multilingual handle mixed-language corpora well); and skipping access controls so that a user in one department can retrieve documents they should not see. Privonis deployments include role-based collection partitioning out of the box to address that last point. Build it right from the start and your private knowledge assistant will be one of the most valuable tools your organisation has ever deployed.