Imagine this: You've created an AI that can discuss quantum physics, write poetry, and crack jokes. But when asked about your company's latest product, it draws a blank. Frustrating, right? Welcome to the cutting edge of AI development, where even the smartest machines need a helping hand. Whether you're a seasoned pro or a curious newcomer, this guide will help you navigate the AI landscape and choose between the game-changing approaches of RAG and fine-tuning.
RAG: Teaching Old AI New Tricks Without Surgery
Retrieval-Augmented Generation (RAG) is a system for creating generative AI applications. It uses enterprise data sources and vector databases to address the knowledge limitations of LLMs. RAG works by using a retriever module to search for relevant information from an external data store based on a user's prompt. The information retrieved is then used as context, combined with the original prompt to create an expanded prompt, which is passed to the language model. The language model then generates a response that includes the enterprise knowledge.
RAG allows language models to use current, real-world information. It deals with the challenge of frequent data changes by retrieving current and relevant information instead of relying on potentially outdated data sets.
Here’s a simple architecture diagram to explain RAG:
and push the boundaries of what's possible in AI development!
SOCIAL SHARE CARD GENERATOR