The Mind AI

Introduction to Retrieval-Augmented Generation Retrieval-Augmented Generation, or RAG, is a key method in artificial intelligence that helps improve the accuracy of language models. It works by combining a knowledge base with a large language model (LLM) to provide more relevant and precise responses. This approach is becoming essential for AI agents that need to handle complex queries. How RAG Systems Work A typical RAG system includes a server that receives prompt queries. This server then searches a vector database to find the closest matching context. The retrieved information is added to the prompt and sent to the LLM, which generates the final output. This process helps the AI understand the context better and produce more accurate results. Challenges in Scaling RAG for Enterprises Enterprises often face challenges when deploying RAG systems at scale. These include managing large volumes of data, ensuring quick response times, and handling many simultaneous user reques...