How To Use RAG in LLM: Implementation, Uses and More
Learn how to use RAG in LLMs, its implementation, key use cases, and benefits to enhance AI performance.
The most advanced large language models (LLMs) in the world, capable of generating human-like text, still struggle with one fundamental flaw—they often “hallucinate,” confidently producing incorrect or nonsensical information. This isn’t just a technical quirk; it’s a critical limitation in fields like healthcare, finance, and education, where accuracy is non-negotiable. Enter Retrieval-Augmented Generation (RAG), a game-changing approach that bridges this gap by integrating real-time, external knowledge into LLMs.
