In a rapidly evolving landscape of highly capable large language models (LLMs), building competitive products in the space feels increasingly challenging. In this session, we explore key features of successful LLM-based solutions from a product and design perspective along three axes: First, we take a birds-eye view, discussing how to identify and build up differentiating factors (moats) that turn an application from “yet another summarisation tool” into a lasting, valuable solution. Next, we zoom in on the product and user level, looking at UX design strategies for cultivating trust and simplicity in the face of a hyped and partially misunderstood technology. Finally, we wrap up the session with actionable product management tips and tricks for guiding and de-risking the development of user-centric LLM-based tools.
As a team lead for AI project management at Merantix Momentum, Hanna Behnke drives the development of customised machine learning solutions for clients in various industries. Working at the intersection between people and technology, it is her goal to bring the latest advances in AI from research to production. Previously, Hanna built data products in the media industry as a technical product manager. She studied Information Technology in Berlin and Computer Science with a focus on Machine Learning at Imperial College London, where she also researched and published in the field of Natural Language Processing.
