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ABOUT THE SPEAKER:
David is a Senior AI/ML Engineer within the Office of the CTO at NetApp, where he’s dedicated to empowering developers to build, scale, and deploy AI/ML solutions in production environments. He brings deep expertise in building and training models for applications such as NLP, vision, real-time analytics, and even classifying debilitating diseases. His mission is to help users build, train, and deploy AI models efficiently, making advanced machine learning accessible to users of all levels.
Before NetApp, he was heavily involved in the AI/ML community, specifically in conversational AI solutions and driving AI platform growth in a DevRel and pre-sales role. David frequently shares his insights at industry conferences and events, offering hands-on guidance for implementing AI/ML in cloud environments. David’s prior experience includes contributing to the Kubernetes and CNCF ecosystems, working hands-on with VMware virtualization, implementing backup/recovery solutions, and developing hardware storage adapter firmware and drivers.
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ABSTRACT:
Most RAG discussions start and end with vector embeddings. That makes sense because vector search is approachable, fast to prototype, and widely supported. But semantic similarity is not the same thing as answer retrieval. When teams rely on embeddings as the default for every use case, they often end up with systems that sound convincing while returning weak, incomplete, or confidently incorrect answers. This talk reframes retrieval as the real design decision in RAG, not a backend detail.
We will walk through the major retrieval options at a high level, including vector, graph, and BM25 approaches, and explain where each one fits. Then we will show why hybrid designs, such as Vector + Graph and Vector + BM25, often produce stronger results by combining semantic context with stronger grounding and greater precision. The goal is to give AI engineers a practical mental model for choosing a retrieval approach based on the shape of their data and the kinds of answers they need, rather than defaulting to embeddings because everyone else did.
WHAT YOU’LL LEARN:
Too many RAG systems are built around a single assumption: use vector embeddings and figure out the rest later. That works until the answers need to be correct. This session shows AI engineers how retrieval choice drives answer quality, why vector search alone often leads to confidently wrong outputs, and how graph, BM25, SQL, and hybrid retrieval patterns can produce better, more grounded results. It is a practical talk for builders who want to move past the default RAG recipe and design systems that answer with more precision and less guesswork.
Business Leaders: C-Level Executives, Project Managers, and Product Owners will get to explore best practices, methodologies, principles, and practices for achieving ROI.
Engineers, Researchers, Data Practitioners: Will get a better understanding of the challenges, solutions, and ideas being offered via breakouts & workshops on Natural Language Processing, Neural Nets, Reinforcement Learning, Generative Adversarial Networks (GANs), Evolution Strategies, AutoML, and more.
Job Seekers: Will have the opportunity to network virtually and meet over 30+ Top Al Companies.
Ignite what is an Ignite Talk?
Ignite is an innovative and fast-paced style used to deliver a concise presentation.
During an Ignite Talk, presenters discuss their research using 20 image-centric slides which automatically advance every 15 seconds.
The result is a fun and engaging five-minute presentation.
You can see all our speakers and full agenda here