Most AI projects fail long before they reach production — not because the models are weak, but because teams focus on technology instead of solving real business problems. This session begins with the executive perspective: the cost pressures, risk concerns, and organizational traps that quietly derail AI initiatives before any code is written. Understanding these business constraints is the foundation for building AI systems that actually work.

From there, we dive into the engineering realities of production AI. You’ll learn how to evaluate LLM performance honestly, choose the right model for your workload, apply practical vector data modeling to keep embeddings meaningful and retrieval accurate, and use the Large Data Model (LDM) pattern to convert raw numeric values into semantic segments for better reasoning and behavior‑based search. We’ll also explore the hidden latency challenges inside retrieval‑augmented generation and how to diagnose them effectively.

Finally, we’ll look at human‑in‑the‑loop patterns that keep AI systems safe, stable, and trustworthy — and why human review remains a core technical requirement even in agentic workflows.

This talk blends business reality with engineering depth to provide a practical, real‑world guide to building AI systems that don’t break — grounded in production lessons, not hype.