RAG (Retrieval-Augmented Generation)
A process in which AI first reads your documents and then responds to the user.
What is RAG?
RAG (Retrieval-Augmented Generation) is a technique that prevents your AI model from "hallucinating" (making things up) by generating answers based on a knowledge base that you provide.
How RAG works
A regular AI model is like a highly educated person who stopped following the news after a certain date and knows nothing about your specific business. RAG solves this problem by presenting relevant information from your database to the model at query time.
- Data preparation: Documents are split into smaller chunks and converted into vectors using an embedding model, then stored in a vector database.
- Query processing: When a user asks a question, the system converts the query into a vector and finds the closest contextual match in the database.
- Answer generation: The found paragraphs are bundled with the question and sent to the LLM, which composes the final answer.
When RAG is the right fit
- Internal knowledge base - employees ask the system instead of digging through documents
- Customer support - a chatbot answering from your manuals and FAQ
- Document analysis - fast search through legal contracts or technical documentation