What is RAG?
Retrieval-Augmented Generation (RAG) is an AI architecture that improves the accuracy of language models by grounding them in external sources of knowledge. Instead of relying solely on the data the model memorized during training, a RAG system retrieves relevant facts from a trusted database and injects them into the prompt before the AI answers.
How does it work?
- Retrieve: When a user asks a question, the system searches an external source (like a vector database, a company knowledge base, or a search engine) for relevant documents.
- Augment: The system takes those retrieved documents and pastes them into the user's prompt as context.
- Generate: The AI reads the augmented prompt containing the hard facts and generates a highly accurate answer.
What is it commonly confused with?
Do not imply that RAG always searches the public internet. While web-search is one form of RAG, enterprise RAG systems usually retrieve data from private, secure company document collections or internal wikis.
Why does it matter?
RAG solves the two biggest problems with Large Language Models: hallucinations and outdated information. By forcing the AI to cite facts from a trusted source, it can answer highly specific, private, or real-time questions accurately without needing an expensive fine-tuning process.