What Is RAG and How Does It Work

Why RAG Exists

AI models like GPT and Claude are trained on massive amounts of public internet data, but they know nothing about your specific business. They do not know your product prices, your return policy, your office hours, or your internal processes. If a customer asks your chatbot about your specific products, the AI would either make something up (called a hallucination) or admit it does not know.

RAG solves this problem by giving the AI access to your information at the moment it needs it. Instead of relying on what the model was trained on, the system searches your documents first, retrieves the relevant sections, and feeds those sections to the AI along with the user's question. The AI then writes a response based on your actual data.

How RAG Works Step by Step

The RAG process has two phases: preparation (done once when you upload content) and retrieval (done every time someone asks a question).

Preparation Phase

When you upload a document or paste text into your chatbot's knowledge base, the system processes it in three steps:

• Chunking: Your document is split into smaller pieces, typically 250 to 2,000 characters each. This ensures each piece is focused on one topic and fits within the AI's context window. See How to Chunk Documents for Better AI Understanding for details on how chunk size affects quality.
• Embedding: Each chunk is converted into a numerical vector (a long list of numbers) that represents its meaning. This is done by an embedding model, which understands language well enough to place similar concepts close together in mathematical space. See What Are Vector Embeddings in Simple Terms for a deeper explanation.
• Storage: The vectors and their original text are stored in a database where they can be searched quickly by meaning.

Retrieval Phase

When someone asks your chatbot a question, the system follows these steps:

• Query embedding: The user's question is converted into a vector using the same embedding model
• Similarity search: The system compares the question vector against all stored chunk vectors and finds the closest matches by meaning, not by keyword
• Context injection: The top matching chunks (usually 3 to 10) are included in the prompt sent to the AI model, along with the user's question
• Generation: The AI model reads the retrieved chunks and the question, then writes a response grounded in your data

Why RAG Is Better Than Alternatives

Before RAG became the standard approach, the only way to give AI custom knowledge was through fine-tuning, which means actually retraining the model on your data. Fine-tuning is expensive, slow, hard to update, and still does not guarantee the model will use your information correctly. RAG is better for almost every business use case because:

• You can update your data instantly without retraining
• The AI cites real information from your documents, reducing hallucinations
• It costs a fraction of what fine-tuning costs
• You can switch AI models without re-uploading data
• It works with any amount of data, from a single FAQ page to thousands of documents

For a detailed comparison, see How Is Training Different From Fine-Tuning.

RAG on This Platform

The AI Chatbot app handles the entire RAG pipeline automatically. You upload documents, paste text, or crawl a website. The platform chunks the content, generates embeddings at 3 credits per chunk, and stores everything in a vector database. When your chatbot receives a question, it searches the relevant embeddings, retrieves matching content, and passes it to your chosen AI model.

The process is invisible to your end users. They ask a question in the chat widget, and the chatbot responds with accurate information from your data. Behind the scenes, the RAG system is searching, retrieving, and augmenting every single response.