Retrieval-Augmented Generation Unlocked: Multi-modal RAG to Agentic GraphRAG Evolution

Snippet/Abstract: RAG (Retrieval-Augmented Generation) optimizes Large Language Models (LLMs) by integrating external knowledge bases, effectively mitigating “hallucinations,” bypassing context window limits (e.g., 32K-128K), and addressing professional knowledge gaps. Evolution into Multi-modal RAG and Agentic GraphRAG enables precise processing of images, tables, and complex entity relationships in vertical domains like medicine, finance, and law, achieving pixel-level traceability. The Ultimate Guide to Full-Stack RAG: From Basic Retrieval to Multi-modal Agentic GraphRAG In the current landscape of artificial intelligence, building a local knowledge base for Question & Answer (Q&A) systems is arguably the most sought-after application of Large Language Models (LLMs). Whether the …