How RAG Systems Retrieve Relevant Data for AI Models

Modern AI applications use retrieval-augmented generation (RAG) to find relevant information from large knowledge bases before sending it to the model. Instead of dumping entire documents into the prompt, systems first identify the most relevant pieces using techniques like keyword search or semantic search. This approach reduces cost, improves speed, and helps avoid hallucinations by grounding the model in specific, reliable data.