What is RAG? Connecting company data to AI with retrieval-augmented generation

RAG (retrieval-augmented generation) lets a language model find relevant information in company documents or databases before answering. It is the most common way to get accurate answers about current or company-specific information the model was not trained on.

How it works

1. Preparation: documents are split into meaningful chunks and each is turned into a numeric vector representing its meaning.
2. Retrieval: when a user asks, the chunks closest to the question's vector are found (usually combined with keyword search).
3. Generation: the retrieved chunks go to the model with the question; it answers based on them and cites sources.

When to use it

• Q&A over internal documents, policies and knowledge bases
• Customer support assistants; see AI in customer service
• Document analysis in law, health or finance
• Frequently changing product and pricing information

When knowledge changes and sources must be cited, RAG is usually more suitable and cheaper than fine-tuning.

Quality tips

• Chunking: too small loses context, too large adds noise; preserve headings and sections.
• Hybrid search: combine semantic and keyword search; important for product codes and names.
• Re-ranking: sort retrieved results by relevance in a second step.
• Citations: show which documents an answer relies on.
• Saying "I don't know": ask the model to say so when the context lacks the answer; see our hallucination guide.

Permissions and privacy

Users should only get answers from documents they may see. Enforce permissions at retrieval time. Assess KVKK requirements for documents with personal data; see our AI and KVKK guide.

Measurement

Measure retrieval accuracy and answer accuracy separately; problems are often in retrieval, not generation; see our model evaluation guide.

Conclusion

RAG is the most practical way to connect company knowledge to AI safely and up to date. Good search is half of a good RAG system.