LLM demos are easy. Production AI is where things get weird.

In this talk, I’ll walk through what actually changes when you move from a clever prototype to an AI feature real people depend on: messy prompts, unclear user intent, unreliable outputs, security constraints, evaluation gaps, and the painful moment when demo accuracy collapses in the wild.

Using real-world lessons from building internal AI assistants, natural-language data tools, and enterprise AI workflows, we’ll cover how developers can design AI systems that are useful, testable, and trusted. We’ll look at practical patterns for scoping use cases, setting confidence boundaries, keeping humans in the loop, and measuring whether the AI is actually helping.

Attendees will leave with a simple framework for deciding when an LLM belongs in a workflow, when it doesn’t, and how to avoid shipping an impressive toy that nobody trusts.