Many AI projects fail because they focus too much on technology instead of solving real business problems. This presentation covers the practical strategy and engineering steps needed to build AI tools that actually work. We will break down common technical pitfalls and how to avoid them, including:

Model Selection: Finding the truth about LLM performance before picking a vendor.
Vector Store Realities: Navigating the hidden challenges of indexing, metadata filtering, dimensionality mismatches, and vector data analysis.
The RAG Challenges: Explore latency issues in RAG.
Azure DocumentDB: Introduction to a new open-source NoSQL database compatible with MongoDB that provides vector search capabilities and can run on-premises.
The Human-in-the-Loop: Understanding why human review remains a core technical necessity.