8-Types of RAGs and their Use Cases | Rakesh Gohel

If you want to learn about how to utilize RAG to the fullest and how to build a capable Agentic RAG systems. Make sure to join, AI Agent Engineering course, where you'll learn to design RAG-powered agents with an enterprise mindset. 📗 Enroll here: https://lnkd.in/gA3zhcfm 1\. Naive RAG - Direct retrieval with no sophisticated filtering or feedback - Example: Customer support chatbots answering FAQs from documentation 2\. Agentic RAG - Autonomous planning, tool use, and dynamic retrieval strategies - Example: Competitive intelligence analysts who autonomously monitor and analyze competitor data 3\. HyDE (Hypothetical Document Embeddings) - Generates "hypothetical" documents for better retrieval matching - Example: Adaptive Technical search where users describe problems differently than usual 4\. Graph RAG - Structures data as knowledge graphs for richer contextual reasoning - Example: Medical support systems generating hypothetical patient cases based on symptom relationships 5\. Corrective RAG - Iterative self-correction to improve retrieval accuracy - Example: Financial report analysis that self-corrects when retrieving across multiple data sources 6\. Contextual RAG - Adapts retrieval by interpreting broader conversational context - Example: Meeting assistants understanding strategic discussions across multiple sessions 7\. Modular RAG - Composable, plug-and-play modules for custom workflows - Example: Custom workflows combining multiple retrieval processes and specialized tools 8\. Hybrid RAG - Mixes semantic search and embedding-based retrieval - Example: Legal research combining exact statutory citations with semantic principle matching 📌 Quick Selection Guide: - Naive RAG → Simple knowledge base Q&A - Agentic RAG → Complex autonomous workflows with tool integration - HyDE → Query-document vocabulary mismatch scenarios - Graph RAG → Interconnected data requiring relationship reasoning - Corrective RAG → High-accuracy requirements with ver