What We Teach in Our AI Certification Program — and Why

Most AI courses teach prompting tricks. Ours starts somewhere less exciting and more durable: how to evaluate whether an LLM-based feature is actually solving the problem, before a single prompt gets written.

The curriculum is built directly from our engineering playbooks — the same rubric we use internally to decide whether a client feature should be handled by a model, a deterministic rule, or a human, is the first module trainees work through.

From there, it moves into practical build patterns: retrieval-augmented generation, structured tool use, and evaluation harnesses that catch regressions before they reach a client's production system.

We deliberately spend a full module on failure modes — hallucination, prompt injection, and silent accuracy drift — because those are the issues that show up months after launch, not in the demo.

The goal isn't to produce prompt engineers. It's to produce people who can ship an AI feature that still works the way it's supposed to a year later.