LEARNER · GLOBAL
What is RAG? How AI systems fetch real-world data to answer questions
RAG (Retrieval-Augmented Generation) is a technique that lets AI models pull relevant documents or data from a database before answering a question. Instead of relying only on its training data, an AI system first searches for fresh, specific info, then generates an answer using both. Think: a smart assistant that looks up facts before responding rather than just guessing.
WHY IT MATTERS
RAG is core to financial AI: compliance officers use it to ground chatbots in policy docs; traders use it to search real-time market research; risk teams use it to audit regulatory filings. Without RAG, LLMs hallucinate.