03/02/2025
Why RAG: LLMs are powerful but often unreliable—they generate generalized content, forget information, fabricate facts, or rely on outdated training data. Retrieval-Augmented Generation (RAG) fixes this by giving models access to external information in real time. Instead of guessing, the AI retrieves relevant facts from sources like PDFs, databases, or internal documents before generating a response.
Here’s how it works: Your query is transformed into a vector and compared against a vector store filled with pre-indexed knowledge. The most relevant documents are pulled in and combined with your query before the LLM responds. This means AI no longer works in isolation but actively references trusted sources, making responses more accurate, up-to-date, and context-aware. With RAG, LLMs don’t just generate—they know.
