How RAG architectures are evolving for intelligent AI systems

RAG is evolving—and it’s changing how we build intelligent AI systems Most people think of Retrieval-Augmented Generation (RAG) as a simple retrieve + generate setup. But in reality, RAG has grown into a sophisticated ecosystem of architectures—each solving real-world problems like context continuity, multi-source retrieval, and dynamic reasoning. In my latest article on DZone, I break down the evolution of RAG architectures step by step: 🔹 Simple RAG → Lookup from a static knowledge base 🔹 RAG with Memory → Maintain context across conversations 🔹 Branched RAG → Route queries to the right data source 🔹 HyDe → Use hypothetical embeddings for better retrieval 🔹 Adaptive & Corrective RAG → Optimize efficiency and ensure accuracy 🔹 Self-RAG → Fetch missing information mid-answer 🔹 Agentic RAG → Multi-agent systems that plan, reason, and act This guide isn’t just theory—I’ve included visual diagrams and practical workflows to make it easy to understand and apply. 📖 Read the full article here: https://lnkd.in/gxyNYv6b If you’re working on LLM-powered apps, copilots, or autonomous agents, this is a must-read. #RAG #LLM #AIagents #GenerativeAI #AgenticAI #KnowledgeGraphs #DZone