Large Language Models have become remarkably capable, yet many AI projects still struggle to deliver reliable results. The problem is often not the model itself, but the quality of the information being provided to it. In this session, we'll take a practical look at document intelligence for production AI systems. Through real-world examples and a live demo using open source tools, we'll compare different approaches for transforming complex documents into structured knowledge ready for retrieval-augmented generation (RAG). Attendees will leave with practical techniques for improving retrieval quality, reducing hallucinations, and building more trustworthy AI systems. The session is tool-agnostic and highly practical, focused on lessons that can be immediately applied to real projects.
Josefina Casanova is a Data & AI Architect at IBM and a Computer Science Engineer passionate about using technology to solve real problems. She spends her days working at the intersection of AI, data and business, helping organizations build solutions that turn messy data into systems that work in production. A lifelong learner, she regularly speaks at tech events, hosts podcasts, and enjoys sharing lessons learned from building with emerging technologies.
