Suman Debnath of AWS Explains How RAG Is Transforming Modern AI (Computers - Information Technologies)
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Item ID 2673767 in Category: Computers - Information Technologies
Suman Debnath of AWS Explains How RAG Is Transforming Modern AI | |
In the latest Discover Dialogues by TechDogs, we sit down with Suman Debnath, Principal Developer Advocate for Machine Learning at AWS, to explore why Retrieval-Augmented Generation (RAG) is becoming essential for scalable enterprise AI. Suman, a leading voice in AI with over 100 global keynotes, simplifies complex concepts for both developers and decision-makers. His key takeaway? Most AI hallucinations are not model failures—they are retrieval failures. Suman compares AI to a librarian: “If your librarian hands you the wrong book, no amount of reading will give you the right answer.” That’s the core issue when AI generates results based on incomplete or irrelevant data. RAG addresses this by grounding AI in accurate, real-world information—pulling data from internal documents, product databases, PDFs, or live systems to enhance model reliability. But AI evolution doesn’t stop at text-based data. Suman introduces Agentic RAG, powered by Vision-Language Models, enabling AI to process multimodal inputs—text, images, charts, and more. With this capability, AI agents can understand forms, visuals, and complex data to deliver smarter decisions. Enterprises in industries like healthcare, finance, and logistics are adopting these intelligent agents to improve operations and enhance automation. This Episode is a Must for: ✔ Product teams deploying GenAI tools ✔ Executives building future-ready AI strategies ✔ Developers navigating AI infrastructure for large-scale systems Suman also shares Colpali, a unique approach to optimize multimodal AI for enterprises blending structured and unstructured data. With expertise in AWS solutions like Bedrock, SageMaker, and vector search, Suman offers a real-world roadmap for making AI scalable and trustworthy. Watch the full episode now at TechDogs.  | |
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2025-07-31 (0.254 sec)