RAG
AI with access to your data
RAG (Retrieval-Augmented Generation) is an approach where, before answering, a language model first retrieves relevant information from your data and only then composes an answer from it. As a result it answers based on specific source material rather than just the model’s general knowledge.
How RAG works
A language model on its own only knows what it was trained on. It does not know your internal documents or the latest changes, and when it lacks information it may make it up. RAG solves this by inserting a retrieval step between the question and the model.
When a question comes in, the system first searches your knowledge base, such as documents, manuals, or frequently asked questions, and selects the most relevant passages. It then adds those to the question, and the model composes an answer from them. The result is an answer grounded in your current and verified source material.
Why businesses need RAG
RAG is today the most practical way to combine the power of language models with a company’s specific knowledge. Instead of expensively retraining a model, you simply maintain an up-to-date knowledge base for the model to draw on.
- Answers come from your documents, not the model’s guesses.
- You update knowledge easily by editing the source material, not the model.
- The risk of made-up answers is significantly reduced.
- Suitable for internal assistants and customer-facing chatbots alike.
Where RAG is used
A typical example is a customer chatbot that answers based on your documentation and price list, or an internal assistant that quickly finds information in company policies for employees. RAG is suitable anywhere you need answers built on specific, frequently changing information. We build such a solution as part of our development services, and its reliability depends on the quality and organization of your source material.
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What is the difference between RAG and fine-tuning a model on our data?
With RAG you do not retrain the model; you simply supply it with relevant source material from your database before it answers. This is usually faster, cheaper, and easier to update, because you only adjust the source material, not the model itself.
Does RAG stop the AI from making things up?
It significantly reduces the risk, because the model answers based on specific source material. It does not eliminate it entirely, though, so for important answers it is recommended to cite the source and verify important information.
What data do we need for RAG to work?
You need an organized knowledge base, such as documents, manuals, policies, or frequently asked questions. The better and more up-to-date the source material, the better and more reliable the resulting answers.