What is RAG and How Does an AI-Based Corporate Knowledge Base Work
In simple terms, Retrieval-Augmented Generation: how to make AI respond based on your documents — without data leaks and hallucinations.
In short, if you don’t have time to read it all
- ✓RAG allows the model to work with current private files without expensive fine-tuning.
- ✓Documents are sliced into fragments and stored as value vectors.
- ✓The model responds strictly based on the found context and provides references to specific points in the document.
- ✓Confidential data does not enter public training datasets.
When a company decides to build an internal assistant for employees or customers, the first thought is usually: “let’s train the model on our files.” But classic fine-tuning is expensive and slow. Change even one policy or price, and you have to retrain the model from scratch, paying thousands of dollars for rented compute.
That’s why the industry moved to RAG (Retrieval-Augmented Generation) — generation enhanced with search.
1. How does RAG work?
Simply put, RAG is like a student in an exam who is allowed to open their notes. They do not memorize everything by heart. They quickly find the right page using the index, read the exact paragraph, and give a precise answer.
It all happens in three steps:
- Indexing (preparing the notes): your corporate PDFs, Word files, or spreadsheets are split into small logical blocks (chunks) of 300–800 words. Each block is turned into a mathematical vector (embedding) that describes the meaning of the text and is stored in a vector database (for example, Qdrant or pgvector).
- Retrieval: the user asks, say, “what is the procedure for returning defective goods?” — and the system instantly finds the 3–5 closest meaning-wise fragments from your database.
- Answer generation (Augmented Generation): the model gets a system instruction: “Answer the customer’s question ONLY based on the text below. If the answer is not in the text, say ‘information not available’ and do not make anything up.”
2. What this gives your business
- No hallucinations: the model is kept within the boundaries of the retrieved context. It will not invent warranty terms that do not exist in your rules.
- Instant updates: replace a file on the server or edit a page in Notion, and within a minute the system is already answering based on the new rules.
- Source links: every answer includes a link to the exact document and section. The user can verify it themselves.
- Security: the documents do not become part of the model’s publicly available weights and stay inside your controlled environment.
Learn more about implementing this technology on the Corporate Knowledge Base and RAG page. Or take a look at a practical example in the Onboarding Scenario.
- Lewis et al.: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- Qdrant Vector Database Architecture Whitepaper
Corporate Knowledge Base and RAG
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Let’s look at how these approaches fit your company’s actual processes.