Teams define their problem statement and build core agent workflows; Mentors available for architecture and framework support.

Abstract: Models need up-to-date facts (data) to solve tasks. But data (retrieval) needs models, too: for semantic search and for ranking top candidates. At this meetup, we will go through the data/model interplay: you will learn how to transform problems into the numeric domain using tensors, and with this, work with text, image, and videos.\nWe’ll do live demos from e-commerce and media. Whether it’s personalizing the shopping experience in real time or finding the next song to autoplay, this session will help you think beyond LLMs—and design retrieval-first GenAI systems that deliver real-world impact.

Organizations develop feedback loops to continuously enhance quality. One such loop is the learning from user interactions with your data, retraining models, deploying new models and learning again. The learning curve to create a loop like this is steep, it requires ML experience and tools. However, most teams can easily provide labeled examples. In-Context Learning (ICL) is a method to add classification examples as input to foundation models (like LLMs).\nThis talk defines an Adaptive ICL strategy using Retrieval for Examples, where the output is used for content retrieval, example set expansion for future model training and real-time user behaviour tracking. Adaptive ICL is hence an easy way for teams to get immediate results with AI, while laying the foundation for more advanced ML loops in the future.