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Building Smarter AI with RAG: AWS Principal Advocate Suman Debnath Explains | |
In the latest Discover Dialogues episode by TechDogs, Suman Debnath, Principal Developer Advocate for Machine Learning at AWS, explains why Retrieval-Augmented Generation (RAG) is becoming the foundation for enterprise-grade AI that delivers accurate, reliable results. With years of AI expertise and over 100 keynote speeches worldwide, Suman simplifies complex AI challenges, making them relatable for business and technical leaders alike. His key message? AI models often fail not because of poor design, but due to weak information retrieval. Suman shares an example: “If your librarian gives you the wrong book, even the smartest reader can’t find the answer.” This highlights why grounding AI models in high-quality, real-world data is essential to eliminate hallucinations and improve accuracy. RAG enables AI to pull reliable information from internal sources like documents, PDFs, databases, or sensor feeds before generating output—making AI decisions far more accurate and enterprise-ready. Looking beyond text, Suman introduces Agentic RAG, where AI agents leverage multimodal data including visuals, charts, text, and forms to make intelligent decisions. This is critical for industries like healthcare, logistics, and finance, where understanding complex, diverse data is vital. Who Should Watch This: ✔ Product leaders implementing GenAI solutions ✔ Executives shaping AI transformation strategies ✔ Developers and engineers building AI infrastructure Suman also discusses Colpali, an innovative approach to optimizing AI search and decision-making in multimodal environments. With his background leading AI solutions at AWS using platforms like SageMaker, Bedrock, and vector search, Suman provides actionable insights for building scalable, production-ready AI. Watch the full episode at TechDogs.  | |
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| Target State: All States Target City : All Cities Last Update : 30 June 2025 4:55 PM Number of Views: 3 | Item Owner : Leslie Dodge Contact Email: Contact Phone: (None) |
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INNetAds > Computers > Information Technologies > Item ID 2688775
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2025-07-01 (0.575 sec)