"Enterprise AI: Making ChatGPT Talk to Your Company Data (RAG)"

"ChatGPT doesn't know our company, we want to teach it about us." The right answer to this sentence is usually not "train the model" but "make the model read documents." The name of this approach is RAG — Retrieval-Augmented Generation. In this article we'll explain the RAG architecture practically.

01. What Is RAG? The Difference from Model Training

Training a model (fine-tuning) is an expensive, lengthy process that must be repeated with every document update. RAG, on the other hand, works on this principle:

1. The user asks a question
2. The system searches the relevant company documents
3. The found documents are given to the model as "context"
4. The model writes an answer based only on this context

The model isn't trained; it just reads the right document at the right time.

02. The Vector Database: The Heart Is Here

At the center of RAG is the vector database. Company documents are split into small pieces (chunks), each piece is converted into a mathematical vector, and stored in the database.

When the user asks a question, the question itself is also turned into a vector, and the nearest documents are retrieved. Tools like Pinecone, Weaviate, and PostgreSQL + pgvector are used for this.

03. Document Preparation: The Hard Part

The quality key of RAG is in document preparation. The difficulties:

• PDFs contain tables and images
• Contracts are grammatically challenging
• The same information can conflict across two different documents
• Document updates require version management

A good RAG project is 70% document preparation, 30% code.

04. Security and Data Isolation

In enterprise RAG, is your data sent to an external model? The answer to this depends on the chosen provider:

• OpenAI Enterprise: a contract that your data isn't used for training
• Azure OpenAI: data stays in your Azure tenant
• Anthropic API: a data agreement specific to the enterprise plan
• On-premise models (Llama, Mistral): data never leaves at all

Choosing the right provider is the project's first decision.