Bastian Grimm’s Post

𝗧𝗵𝗿𝗲𝗲 𝗣𝗿𝗶𝗺𝗮𝗿𝘆 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 𝗔𝗴𝗲𝗻𝘁 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 🤖 Agentic RAG systems employ three key strategies - routing, query transformation, and query planning - to create 𝘮𝘰𝘳𝘦 𝘥𝘺𝘯𝘢𝘮𝘪𝘤, 𝘢𝘥𝘢𝘱𝘵𝘢𝘣𝘭𝘦, 𝘢𝘯𝘥 𝘤𝘰𝘯𝘵𝘦𝘹𝘵-𝘢𝘸𝘢𝘳𝘦 𝘴𝘰𝘭𝘶𝘵𝘪𝘰𝘯𝘴 compared to traditional RAG. These strategies allow for precise data retrieval, optimized responses, and the efficient handling of complex queries. • 𝗥𝗼𝘂𝘁𝗶𝗻𝗴: Acts as a traffic controller, directing queries to the most relevant data source or tool based on context and intent. For example, a query about company policies might be routed to internal databases, while industry trends are directed to external knowledge bases. This approach improves accuracy by targeting relevant sources and increases efficiency by avoiding unnecessary searches.    • 𝗤𝘂𝗲𝗿𝘆 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: Refines the user's input to better align with the structure and content of target data. Techniques include rephrasing for clarity, expanding queries with additional keywords, or breaking down complex queries into simpler components. These refinements enhance precision, resolve inconsistencies, and ensure results closely match the user's intent.    • 𝗤𝘂𝗲𝗿𝘆 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴: Breaks down multi-faceted queries into smaller, manageable tasks, executed sequentially or in parallel. For instance, a request for a travel itinerary might be divided into sub-queries for flights, hotels, and attractions, with results integrated into a comprehensive response. This enables the system to handle intricate queries while delivering well-structured, actionable answers. By combining these strategies, agentic RAG systems go beyond traditional methods, providing advanced solutions for complex and diverse information needs.

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✌️Olaf Kopp ☀️

Co Founder Aufgesang 🚀SEO+LLMO+Semantic Search+ E-E-A-T🔎Customer Journey Management+Content Marketing🤓Founder SEO Research Suite🏠based in 🇩🇪 & 🇵🇹

2w

Very imteresting. Query refinement or transformation is also happening in traditional retrieval systems. It seems with agents query processing is happening on stereoids. You speak of queries here. But according to generative AI prompts are used. Why you decided to call it query here not prompt?

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