Ben Greenberg
Couchbase
|hummusonrailsHow to Decipher User Uncertainty with GenAI and Vector Search
User expectations are sky-high, while at the same time users have increasing difficulty articulating their complex needs in a simple search bar on a website. This talk dives into leveraging generative AI and vector search to transform vague user queries into results the user actually wanted, even if they did not know initially what they clearly wanted. We will explore why traditional search methods fall short in grasping user intent and address the common problems users face with ambiguous search queries. Learn how GenAI generates embeddings to capture the context and semantics of queries. This talk though is not only about the theory. In the talk you will be shown practical examples of how to generate embeddings, and how to set up vector indexes. See how these advanced search capabilities can transform user interaction and, as a result, business outcomes, making user uncertainty into certainty with GenAI and vector search.
Bio
Ben spent a decade in adult education, community organizing, and non-profit management before transitioning to software development. He is currently a Senior Developer Advocate at Couchbase and serves on the board of Ruby Central. Previously, Ben held roles in developer advocacy and education at companies such as Vonage, New Relic, and Parity Technologies. He is the co-author of a book on technology and ethics and the author of an upcoming book on mastering vector search for developers, to be published by The Pragmatic Bookshelf. Ben actively contributes to open-source projects and writes extensively on the intersection of technology, ethics, and community.