Large Language Models (LLMs) promise major efficiency gains, but their behavior can be inconsistent without a clear understanding of how they generate results. This session offers a grounded look at what shapes LLM outputs, when they can be trusted, and where their limitations intersect with governance, metadata quality, and organizational risk. We’ll also examine how these systems handle conversational history and what that means for privacy and responsible use.

Building on that foundation, the session shows how to guide GenAI so it becomes a reliable partner. Through focused examples, we’ll explore how effective prompting, stronger metadata, and alignment with controlled vocabularies can improve search, retrieval, and overall reliability. Attendees will leave with techniques they can apply immediately in their own environments.