You asked ChatGPT a question about your own product. He replied in a confident tone and completely made things up. Because he doesn't know your price list, your procedures or your documentation. It's not the AI's fault, it's the way you wired it up. RAG solves exactly this problem. What is RAG and when does your company really need it? One by one.
In short
RAG (Retrieval-Augmented Generation) is a way to connect an AI model to your company's knowledge base. The model first searches for the answer in your documents and only then responds with a link to the source. Thanks to this, he knows your price list and procedures, and when there is no basis in data, he says he doesn't know, instead of making things up. Hence the main difference from regular ChatGPT: RAG responds from your knowledge, not from general knowledge from the Internet.
What is RAG?
RAG is an abbreviation of Retrieval-Augmented Generation, i.e. generating answers supported by search. This is a technique that was described in 2020 by researchers in... scientific work of Lewis and colleagues. It sounds academic, but the idea is simple.
RAG combines two things: a search engine for your documents and a language model that creates an answer from them. Instead of asking the model „what do you know about this from the internet”, you ask „what do you know from these documents of mine”. The model receives the question along with excerpts of your knowledge base and bases the answer on them.
Imagine a new salesperson who received a key to a binder with all the company's knowledge: price lists, specifications, history of arrangements with customers, instructions. You ask him anything and he finds the right page and reads from it instead of guessing. RAG is that salesman, only he responds within a second and never goes on vacation.
Definition: RAG is an architecture in which the AI model, before providing an answer, searches for matching excerpts in the indicated knowledge base and generates an answer solely on their basis, providing the source.
How does RAG work? Three steps
RAG breaks down responding into three steps:
1. Indexing: Your documents (PDF, database, emails, pages) are divided into excerpts and saved in a way that allows you to quickly search them by meaning, not just by keyword. This happens once and then updates with changes.
2. Search: when someone asks a question, the system searches for the parts of your documents that most closely match that question. Not the whole binder, just the two or three pages that are on the topic.
3. Generation with a quote: the model receives the question plus the found excerpts and composes a concise answer from them. He also includes a link to the document he used, so you can verify it.
Three steps. Effect: an answer based on your knowledge, with the ability to check where it comes from.
How is RAG different from regular ChatGPT?
This question is asked most often because many people have already tried ready-made tools and got burned. The difference is in three places.
Knows your data. The finished model was trained on general data from the Internet. He doesn't know your price list for this quarter or arrangements with a specific client. RAG connects it to your knowledge base, so it responds to what you have at your disposal.
Shows the source. Regular chat gives you the answer without any indication of where it came from. RAG adds a link to the document. A salesperson or customer can click and check instead of yesing their word for it.
He says „I don't know”. This sounds trivial, but it is crucial. When the model has no basis in your data, a well-built RAG responds that there is no information, instead of stating an untruth in a confident tone. In a company, self-confident untruth costs more than an honest „I don't know.”.
Why does RAG need it? Four signs that it makes sense
RAG is not for everyone and not for everything. But if you recognize at least two of these symptoms, it makes sense to talk.
- Knowledge resides in people's heads and in hundreds of files. The salesperson calls the technologsst because he does not know whether the product fits the machine. Onboarding a new person yeses months because the knowledge is not in one place.
- Someone is manually sorting emails and reports. The contact box is a bag: inquiries, complaints, invoices. AI-based classification can mark this and direct it further.
- Documents read and transcribed by hand. Invoices, orders, specifications. AI reads the document and extracts data, and the human only concompaNoss uncertain cases.
- You tried the ready-made chat, but he was making it up. Because he didn't know your company. RAG connects the model to your knowledge and this problem disappears.
If your main goal is to carmate repetitive tasks, rather than just answering from knowledge, start with process carmation, and add AI where it really helps.
Is RAG making this up?
Honest answer: hallucinations of any technology cannot be reduced to zero. But they can be significantly limited and caught, and that is the whole difference between a toy and a company tool.
In a well-built RAG, answers are based on found excerpts, the model is forced to cite the document, and quality is measured by your real questions. You also set a threshold below which the model says it doesn't know, instead of guessing. You assess the quality on a prototype, on a Sample of your documents, before you decide on full implementation.
RAG and GDPR: where is my data?
This question determines the entire architecture, so it is asked at the beginning, not at the end.
When the data is sensitive (personal data, finances, internal documents), the model can be placed on your server and then the data does not go beyond your infrastructure. When there are no such restrictions, cloud models are used, which is cheaper and faster. A well-designed architecture also allows you to switch to another model when prices or supplier policies change, so you are not dependent on one supplier.
Where to start with RAG?
From one specific question: what should AI do and on what data. Not „let's implement AI”, but „I want a new salesperson to receive answers from our technical documentation in seconds”. The narrower the first case, the faster you will see if it works on your data.
W JSON Crew we do it in stages with a working prototype on your data, before the full version is created, and the payment is divided into four installments. The full scope and process is described on the website AI implementations. If you also want to connect AI with a sales system, see also What is Customer Relationship Management? and What is Configure, Price, Quote?.
Want to check if RAG makes sense on your data?
We will show a working prototype on a Sample of your documents before you pay for full implementation.
RAG is not magic. It's AI connected to your knowledge.
The entire advantage of RAG comes from one thing: the model stops guessing from the Internet and starts answering from what your company already knows. Answer with a link to the source, an honest „I don't know” instead of making things up, data where it's supposed to be. This is the difference between AI for show and AI that does the job.
