---
title: RAG systems in simple words
url: https://blog.krasovskiy.team/en/rag-systems-in-simple-words/
date: 2026-07-02
lang: en
source: blog.krasovskiy.team
---

# RAG systems in simple words

Imagine that you ask a colleague about the project, and he does not just answer from memory, but instantly flips through hundreds of documents, finds exact numbers and even warns: "Here in the contract is clause 4.2 - it can become a problem." This is how RAG systems work: they do not invent answers, but look for them in real data - technical manuals, internal databases or even 10-year-old chat archives. Instead of training a model from scratch (which costs from $50,000 per month), RAG simply connects it to your documents — and after a week you get a chatbot that knows your business better than a newbie.

## What are RAG systems: a simple explanation

**RAG** is an acronym for **Retrieval-Augmented Generation** (Retrieval + Augmented Generation). Imagine an intelligent assistant that first looks for the necessary information in the directories, and then formulates an answer based on it. Here's the whole point: RAG systems combine two stages — **data search** (retrieval) and **text generation** (generation) — to give more accurate and reasoned answers than regular chatbots.

For example, if you ask: _"What are the benefits of electric cars in 2026?"_, the RAG system will first find fresh data from technical reports, news or scientific articles (even if it was not present in its initial training) and then form an answer based on this data. It's like a student rereading notes before an exam rather than relying solely on memory. Thanks to this, RAG systems "invent" facts less (so-called _hallucination_ in AI) and more often give verified information.

RAG systems work according to a simple but effective scheme: first they find the necessary information, then generate an answer based on it. Let's analyze this process using the example of the query _"Which flu medicines are the most effective in the winter of 2026?"_.

1. **Limitations of chatbots:** work only with data that they "know" from training; do not know how to update information; often make up facts when they don't have an answer.
2. **Real time.** Today, RAG works with pre-indexed data. Soon there will be systems that analyze streaming data: news, stock quotes, social networks — and instantly generate answers. _Bloomberg_ is testing this approach for financial analysts: the delay has decreased from 15 seconds to 0.8.