RAG is the technique behind every useful enterprise AI deployment. Without it, AI answers from general training data and frequently hallucinates. With it, AI answers from your specific documents — accurately, with citations. Here is how it works and why it matters.
Large language models are trained on vast amounts of public text. They know a great deal about the world in general. But they know nothing about your company's internal documentation, your product specifications, your customer data, or your proprietary processes — because that information was not in their training data.
When you ask a general-purpose AI "what is the onboarding process for a new enterprise client?" it will give you a plausible-sounding answer drawn from general business knowledge. That answer will be wrong for your organisation. If your specific onboarding process involves a custom step that is unique to your business, the AI has no way to know it.
This is where RAG changes everything.
RAG does not make the language model smarter. It makes it better informed — specifically about your organisation. The model's general intelligence stays the same; its answers become specific to your context.
When you ask a question in a RAG-powered system, two things happen before the language model generates any text:
The system searches your knowledge base — your uploaded documents, connected databases, synced tools — for the chunks of text most relevant to your question. This is done using vector similarity search: your question and your documents are both represented as mathematical vectors, and the system finds the closest matches.
The retrieved text is added to the prompt sent to the language model — alongside your original question. The model now has specific, relevant context to work with, not just its general training.
The model generates an answer grounded in the retrieved context, and typically cites the source documents it drew from. The answer is accurate because it is based on your actual data, not a guess.
The retrieval step works because of vector embeddings. When you upload a document to a RAG system, the text is broken into chunks and each chunk is converted into a numerical vector — a list of numbers that captures the semantic meaning of the text. These vectors are stored in a vector database.
When you ask a question, that question is also converted into a vector using the same embedding model. The system then finds the stored chunks whose vectors are closest to the question vector — which are typically the chunks most semantically related to what you asked.
This is why RAG can find relevant information even when the exact words in your question do not appear in the document. It is searching by meaning, not by keyword.
The quality of a RAG system is almost entirely determined by the quality of the knowledge base. A few principles that make the biggest difference:
A common question: should we fine-tune the model on our data, or use RAG? For almost all business use cases, the answer is RAG. Fine-tuning bakes knowledge into the model weights — which means you need to retrain every time your data changes. RAG retrieves from a live knowledge base that you update continuously without touching the model.
Fine-tuning is useful when you need the model to adopt a specific communication style or behaviour consistently. RAG is useful when you need accurate, current factual answers from your own data. In practice, most enterprise deployments need the latter.
Open Enterprise's RAG pipeline handles the full lifecycle: document ingestion (PDFs, DOCX, Markdown, HTML, CSV, and 20+ other formats), chunking, embedding, vector storage, retrieval at query time, and cited responses in the chat interface. The vector database defaults to LanceDB (no separate service to run) with options to switch to Pinecone, Weaviate, Qdrant, or five others for scale.
Connectors keep the knowledge base current: sync from Google Drive, Confluence, Notion, SharePoint, and dozens of other sources automatically, so the RAG system always reflects your latest documentation rather than a snapshot you uploaded six months ago.
Open Enterprise ships with a full RAG pipeline in one Docker container. Upload your documents, connect your tools, and get accurate answers from your own data.
Get Started Free →