RAG (Retrieval-Augmented Generation) is a method in which an AI system first searches your knowledge base for relevant information, and only then adds it to the instruction for the model that writes the answer. Instead of relying on what the model "remembers", you hand it the right pages before it answers.
In short
- RAG is a method: first you pull the passages relevant to the question from the business's documents, and then the model answers based on them.
- It does not retrain the model. The retrieved passages are added to the instruction, with every new question.
- It suits cases with a lot of internal information: price lists, procedures, FAQs, product documents.
- It reduces errors but doesn't eliminate them. The quality of the answer depends on the quality of the documents and the retrieval.
The illustration: an open-book exam
One student is examined orally, based on what they remember. A second student is examined with the books on the desk: they get a question, flip to the right place, read and answer.
A language model without RAG is the first student. With RAG it is the second. It doesn't get smarter, but it has the right page in front of it. And just like in an exam, everything depends on whether it opened the right page.
How RAG works
According to Anthropic's explanation, the knowledge base is prepared in advance: it is split into small chunks of text, each chunk is converted into a numeric representation called an embedding, and everything is stored in a database that can be searched. When a question comes in, the chunks closest to the question are found and added to the instruction.
From your documents to an answer
- Preparing the baseDocuments are cut into chunks and stored for search
- QuestionA customer or employee asks
- RetrievalThe chunks closest to the question are chosen
- AnswerThe model answers from the chunks it received
OpenAI also offers this as a built-in service: its file search tool works against collections called vector stores, and searches by meaning as well as by keywords. You can limit how many results come back and filter by file attributes.
Why does RAG matter for a business?
- Customer service. Answering questions about shipping, returns and opening hours from the current document, not from a guess.
- Internal procedures. A new employee asks "how do we handle a refund?" and gets an answer from your own procedure.
- Catalog and price list. Product questions are answered from the updated file, not from what the model once learned.
- Quotes and documents. Preparing a draft based on previous quotes and standard terms, for you to review.
- Knowledge sitting in email and documents. Instead of searching by hand, you ask and get the relevant passage.
Without RAG
A student examined orally
- Answers from what it learned in training
- Doesn't know the business's documents
- May fill in details by guessing
With RAG
An open-book exam
- Gets relevant passages along with the question
- Answers based on your documents
- You can show which passage the answer came from
Common myths about RAG
Myth"RAG trains the model on my information"
In practiceThe model doesn't change. The retrieved passages join the instruction with every question, and stay only as long as they are in the instruction.
Myth"With RAG there are no hallucinations"
In practiceIt reduces them, but doesn't eliminate them. If the wrong passage is retrieved, or the document itself is wrong, the answer will be wrong. Anthropic notes that techniques like these reduce hallucinations and don't remove them completely.
Myth"You need RAG for everything"
In practiceIf all the relevant information is short and fits in the instruction, you can simply attach it. RAG is needed when the knowledge base is too big to put in all at once.
Myth"You upload files and it works"
In practiceAnthropic warns that cutting documents into chunks can lose context. A sentence like "revenue grew" means nothing without knowing which company and which period it refers to. Well-organized documents help.
Related concepts
- LLM: the language model that answers in the end
- Hallucination: what happens when the model doesn't know
- Token: why you can't put everything in at once
- MCP: connecting a model to information sources and tools
- AI agent: a model connected to the business
Platin Digital is an OpenAI Select Partner. When we set up an agent for a business, our first question is which documents it needs to know, and who is responsible for keeping them up to date.
Frequently asked questions
What is RAG?
RAG, short for Retrieval-Augmented Generation, is a method in which an AI system first searches a knowledge base, such as the business's documents, for relevant passages, and adds them to the model's instruction before it answers. That way the answer rests on a source and not only on what the model learned in training.
What is the difference between RAG and training a model?
In training, the model itself changes. In RAG the model stays as it is, and receives the matching passages with every question. So you can update a document and the answers update, without retraining.
Is RAG suitable for a small business?
When there is a real knowledge base, such as repeated questions, procedures or a catalog, yes. When all the information fits on one page, you can attach it to the instruction without RAG. Start with the questions that come up most often.
Does RAG guarantee correct answers?
No. It improves the odds, but the answer depends on the right passage being retrieved and the document being up to date. We recommend asking the model to cite its source, letting it say that it doesn't know, and checking important answers. More in the explainer on hallucinations.
