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Modern AI with RAG: AWS’s Suman Debnath Shares Expert Insights | |
Generative AI is evolving fast—and according to Suman Debnath, Principal Developer Advocate for Machine Learning at AWS, Retrieval-Augmented Generation (RAG) is the foundation businesses need to make AI practical, reliable, and scalable. In this exclusive episode of Discover Dialogues by TechDogs, Suman explains why grounding AI in accurate, real-world data is critical to eliminating hallucinations and boosting performance. His analogy? AI models are only as good as their information source—“If your librarian gives you the wrong book, you won’t get the right answer.” That’s where RAG comes in. By retrieving verified data from internal systems like documents, databases, or sensors before generating outputs, AI can deliver more reliable and context-aware results. Suman also introduces Agentic RAG, powered by Vision-Language Models, designed for enterprises managing both structured and unstructured data. Whether your AI needs to analyze forms, charts, text, or images, Agentic RAG ensures intelligent decisions based on comprehensive inputs. The conversation highlights how industries like healthcare, logistics, and finance are already adopting multimodal AI agents to streamline operations and improve accuracy. Why You Should Tune In: ✔ Learn how RAG strengthens AI output with real-time data ✔ Explore the future of multimodal AI combining text, visuals, and audio ✔ Get actionable insights to build scalable, enterprise-grade AI ✔ Understand the evolving role of AI agents in modern businesses Suman’s experience leading AI initiatives at AWS with platforms like Bedrock, SageMaker, and vector search makes this session essential for product leaders, engineers, and executives investing in AI. Watch the full episode now on TechDogs.  | |
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| Target State: All States Target City : All Cities Last Update : Jun 30, 2025 6:48 AM Number of Views: 9 | Item Owner : Leslie Dodge Contact Email: Contact Phone: (None) |
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Hot-Web-Ads > Computers > Information Technologies > Item ID 15661895
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2025-06-30 (0.336 sec)