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What is RAG in simple words?
RAG stands for Retrieval Augmented Generation. It sounds absurd, but in reality everything is much simpler. Imagine a student preparing for an exam. You cramme everything and then answer from memory (sometimes you think about what you don’t know). Another comes with notes, finds the necessary information and gives an accurate answer based on these materials.
RAG is the second student. The technology enables the language model (LLM) Don’t make up an answer from your head, but first look for information in external sources – databases, company documents, directories – and only then formulate an answer based on this data.
You ask the neural network: “How much do Moscow-Sochi train tickets cost?” An ordinary model begins to fantasize because its training set does not contain such data. And the RAG system will first get into the current price database of Russian Railways, get new data from there and only then answer: “Today a reserved seat costs 3,500 rubles.” The difference is colossal.
Why RAG appeared: the problem of hallucinations in neural networks
Language models can generate text. Sometimes they are too good at it. The problem is that they I don’t always understand the difference between truth and fiction. The neural network is trained on huge amounts of text from the Internet and remembers patterns, but not facts. If ChatGPT doesn’t know the exact answer, maybe he’s just making up plausible nonsense. These are called hallucinations.
Let’s say you work for a company and ask the company bot, “What is our vacation approval process?” If the bot is based on a regular LLM without access to internal documents, it can output general wording along the lines of “normally needs to be coordinated with the manager”. But in reality, you may have a complex system with electronic applications, three-tiered approvals, and special rules for remote workers. The bot will lie and no one will notice until someone gets into an uncomfortable situation.
Another problem is outdated data. The GPT-4 model was trained on texts up to a specific date. Everything that happened after that doesn’t exist for them. New laws, changes to company products, new scientific findings – the model knows nothing about this. And he will confidently talk about what happened a year ago.
RAG solves both problems at once. The model no longer invents things because it is always based on a specific document or data. And gets access to current information – even if it is updated hourly.
This is how RAG works: from question to answer in three steps
Now let’s look at the mechanics. RAG consists of three components that work like a cohesive pipeline.
Step 1. Finding the necessary information (retrieval)
You ask a question. The system converts it into a vector representation (embedding) – a series of numbers that reflect the meaning of your request. The knowledge base is then searched for similar vectors. This can be a vector database like Pinecone, Weaviate or a regular document search engine.
The knowledge base is pre-indexed in the same way – each paragraph or document is represented by a vector. The algorithm finds several of the most suitable text fragments. For example, from instructions on working with a CRM system, from an FAQ on technical support or from a medical textbook.
Step 2: Pass the context to the language model
The fragments found are fed into the language model along with your original question. In fact, the model receives not just the question “How do I reset my password?”, but a whole package: your question plus three paragraphs from the technical documentation, which describes the process of resetting the password step by step.
The model reads this context and understands: I have accurate information, there is no need to invent anything.
Step 3. Generate the exact answer (Generation)
Based on the context provided, the model formulates an answer. She does not fantasize, but rather retells or synthesizes information from documents. If the reference book says: “Click on the “Recover password” button and enter your email address,” the model will output exactly this algorithm, which may be reworded for convenience.
The end result is an answer that is accurate, relevant and verifiable. You can even add links to sources so the user can see where the information came from.
How is RAG different from traditional neural networks?
To better understand the difference, let’s compare the classic LLM and RAG systems.
| parameter | Regular LLM | RAG system |
|---|---|---|
| Source of information | Training set only | Training set + external databases |
| Data relevance | Become obsolete over time | Updated in real time |
| Hallucinations | Often makes up facts | Relies on documents, lower risk |
| Verifiability of answers | The source cannot be verified | You can specify the source document |
| flexibility | Needs to be retrained for new data | It is enough to update the knowledge base |
| Reaction speed | Fast | A little slower due to the search phase |
The main advantage of RAG is They control the sources of information. Don’t like the answer? Correct the document in the database and the model reacts differently. This doesn’t work with a regular LLM – you have to retrain the model, which costs a lot of money and time.
Where RAG is used: practical examples
RAG technology is particularly valuable when accurate and timely information is important. Here are four scenarios in which it performs best.
Corporate knowledge bases
The company has accumulated gigabytes of internal documentation – regulations, instructions, security guidelines. Employees drown in this sea of information and spend hours searching for an answer to a simple question. The RAG-Bot immediately finds the desired paragraph in every document and gives a clear answer. Practical for HR (vacation policies, compensation), IT (device setup instructions) and lawyers (contract search).
Helpdesk chatbots
The customer asks: “How can I return the goods?” The bot connects to the technical support knowledge base, finds the current return policy (which was updated a week ago) and provides precise instructions. There is no need to retrain the bot every time the rules change. Simply edit the document in the database.
Analysis of documents and contracts
Lawyers and analysts work with hundreds of contracts. You need to quickly find all references to a specific condition or compare clauses from different contracts. The RAG system extracts the necessary fragments from all documents and generates a summary. This saves days of work.
Medicine and law
Doctors and lawyers cannot afford to make mistakes due to the model’s hallucinations. The RAG gives you a tool that refers to specific medical research, case law or legislation. The specialist sees the source and can verify the information. Trust in the system is growing.
Advantages and limitations of RAG
Every technology has strengths and weaknesses. RAG is no exception.
The advantages are obvious. You get up-to-date information without having to retrain the model. Hallucinations are reduced – the model invents less when documents are supported. Answers become verifiable – you can cite the source. Flexibility increases – the database has been updated and the system takes the changes into account immediately. This is particularly important in dynamic fields such as law, medicine or technical support.
But there is also Disadvantages that cannot be ignored. The quality of the answers directly depends on the quality of the search. If the system did not find a relevant document, it gives a bad answer or starts fantasizing again. Setting up a search engine requires expertise – you need to correctly index documents and select vector search algorithms. The speed of work drops – every request goes through the search phase, which leads to a delay of a second or two. For Chatbots that can be critical. And finally the costs. Vector databasesstorage EmbeddingsAPI calls are all more expensive than just running LLM.
RAG is not a panacea. If your role is to write creative texts or communicate on abstract topics, a regular LLM is more suitable. But where accuracy and reference to facts are required, RAG becomes indispensable.
Here’s how to start using RAG in your project
Let’s say you decide to implement RAG. Where should I start? The process goes like this: First, you select and prepare data sources – these can be PDF files, knowledge bases, documentation in Confluence or Notion. These documents must then be divided into fragments (chunks) – usually by paragraphs or semantic blocks. Each fragment is converted into a vector using an embedding model (e.g. OpenAI Embeddings or open alternatives like Sentence Transformers). These vectors are stored in a special vector database – Pinecone, Weaviate, Milvus or even in PostgreSQL with the pgvector extension.
Next, select a language model to generate answers. GPT-4 is enough Claudeor open models like lamaMistral. It is important to consider the cost of requests and delays – cloud APIs are more convenient but more expensive, on-premises models are cheaper but require servers.
To connect all components use ready-made frameworks. LangChain – the most popular tool, there are ready-made modules for RAG. LlamaIndex (formerly GPT Index) specializes in indexing documents and building RAG systems. Both frameworks support Python and integration with various databases and models.
Start small: Take 10-20 documents and create a simple prototype. Check how the search works and how relevant the system finds. Then scale – add more data, improve search algorithms, experiment with parameters. RAG requires iterations; It won’t be perfect right away.
Conclusions: Who needs RAG and when?
RAG is not a fashion trend, but a solution to a specific problem. If your task involves large amounts of documents, requires frequent data updates, or requires high accuracy of answers, RAG is ideal. Help desks, company knowledge databases, medical and legal advice, contract analyzes – technology shows its strengths wherever the costs of errors are high.
However, if you need a creative assistant to write texts, generate ideas or just a conversation partner, a regular LLM is a better and cheaper solution. RAG adds complexity and cost, so use it where it’s really needed.
Primarily – Understand your task. Do you need accuracy and relevance? Implement RAG. Do you need creativity and flexibility? Stick with the classic model. And ideally combine both approaches depending on the type of request. This way you get the best of both worlds.
