AWS’s Suman Debnath on RAG and the Future of AI Architecture (Computers - Information Technologies)
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Item ID 9749714 in Category: Computers - Information Technologies
AWS’s Suman Debnath on RAG and the Future of AI Architecture | |
In this TechDogs Discover Dialogues episode, Suman Debnath, Principal Developer Advocate for Machine Learning at AWS, explores why Retrieval-Augmented Generation (RAG) is the foundation of scalable, reliable AI for modern enterprises. Suman, an AI expert with over 100 global talks to his name, explains that most AI failures stem from poor data retrieval, not flawed models. He puts it simply: “If your librarian hands you the wrong book, you’ll never get the right answer.” That’s exactly how AI hallucinations happen when the wrong information feeds the system. RAG solves this by grounding AI outputs in relevant, trusted data pulled from documents, databases, product information, and more. This ensures better decision-making and reduces errors in AI responses. Suman also highlights the rise of Agentic RAG—AI agents capable of processing multimodal data, including text, images, charts, and forms. This is crucial for industries like healthcare, finance, and logistics, where AI needs to analyze complex, varied information to provide accurate results. The conversation also introduces Colpali, an innovative approach that enhances how AI retrieves and processes multimodal data for smarter, real-time decisions. This Episode Is a Must-Watch for: ✔ Product teams deploying Generative AI ✔ Technical leaders building scalable AI systems ✔ Enterprises seeking to improve AI decision-making With practical examples and real-world insights from AWS tools like Bedrock, SageMaker, and vector search, Suman provides a blueprint for making AI trustworthy and enterprise-ready. Don’t miss the full discussion—watch it now on TechDogs.  | |
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| Target Nation: All Nations Target City : All Cities Last Update : 30 June 2025 12:27 PM Number of Views: 116 | Item Owner : Leslie Dodge Contact Email: Contact Phone: (None) |
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2025-07-30 (0.259 sec)