Many AI projects start with high hopes and ambitious goals. However, when the focus shifts to AI's potential rather than addressing real, urgent business challenges, these initiatives often fail.

In this presentation, we will explore the essential technical and strategic foundations for transforming ambitious visions into actionable, successful AI projects. We will examine typical engineering "gotchas," including:

The Model Selection: How to find the "ground truth" on LLM performance and reasoning capabilities before choosing a provider.
Vector Store Reality Check: 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: Why human intervention is an essential technical requirement.