What is RAG? AI that answers from your own documents
Language models don't know what they haven't seen. RAG means finding the most relevant parts of your documents before answering and handing them to the model. Here is the idea, the benefit and the limits.
If you ask a language model about your company's returns policy, it will either guess or give a generic answer. The model hasn't seen that information. RAG is the way to solve this without retraining the model.
RAG in plain words
RAG stands for Retrieval-Augmented Generation. How it works:
- The user asks a question.
- The system finds the most relevant pieces in your documents.
- Those pieces are handed to the model along with the question.
- The model answers relying on that text.
It is like an open-book exam. The model doesn't need to have memorised everything. It only has to read the right passage.
Why not rely on the model alone
- The model's knowledge is old or generic. It doesn't know today's price, your stock or your internal policy.
- Invention. When a model doesn't know, it may confidently make something up.
- No source. You can't say where an answer came from.
- Retraining is expensive. And it would have to be repeated each time a document changes.
With RAG, updating knowledge means changing a document, not training a model.
The parts of a RAG system
| Stage | Job |
|---|---|
| Preparing documents | Clean them and split into meaningful pieces |
| Indexing | Turn pieces into numeric representations (embeddings) for semantic search |
| Retrieval | Find the pieces relevant to the question |
| Answering | The model answers from those pieces |
| Citation | Show where the answer came from |
Where RAG works well
- Customer support based on documentation and policies
- A sales assistant that answers from a catalogue
- Search across an organisation's internal knowledge
- Guidance on rules and procedures
Limits you should know
RAG isn't magic. Its quality depends on:
- Document quality. Incomplete or contradictory documents give incomplete or contradictory answers.
- Chunking. Pieces that are too large or too small damage retrieval.
- Multi-step questions. An answer that must combine several documents is harder.
- Persian text. Retrieval and indexing quality has to be tested on real Persian samples, not assumed.
- It can still be wrong. It needs ongoing evaluation.
RAG in a product
In a product like Pasokhinoo, the principle is that the assistant answers from the store's real knowledge, not from the model's general memory. That is the thinking behind RAG. Whether AI is core to the product rather than decoration depends on this kind of design.
How to start
- Choose a small, clean set of documents.
- Write twenty to thirty real questions with correct answers.
- Measure retrieval and answering separately: was the right piece found? Did the model read it correctly?
- Once stable, expand the knowledge.
The takeaway
RAG connects a model to your documents so answers come from a real source. Its quality depends on document quality and evaluation, not just the model. For any intelligent product that has to answer "from your information", it is the logical starting point.
To design an assistant built on your own knowledge, talk to me.