James Okonkwo
AI Engineering Lead
June 15, 2026 · 6 min read
Twelve cohorts. Five hundred students. One question we kept asking at the end of every capstone review: why do some teams ship a working AI product in six weeks while others spend the same time still tuning prompts?
The teams that ship start with evaluation
The strongest predictor of success wasn't prior ML knowledge or engineering skill. It was whether a team built an evaluation set before writing their first prompt. Teams with twenty test cases iterated ten times faster than teams eyeballing outputs.
This is now week one material in our Prompt Engineering program. Build the eval first. Then prompt. Then measure. Then repeat.
Retrieval beats cleverness
Our second finding: the winning projects were rarely the most architecturally ambitious. A well-chunked document store with a simple retrieval loop outperformed elaborate agent pipelines almost every time. Students who resisted the urge to build multi-agent systems early shipped sooner and with fewer failure modes.
The lesson we now teach explicitly: earn complexity. Start with the dumbest architecture that could work, then add a component only when the evaluation proves you need it.
What changes in our curriculum
Starting next cohort, every project begins with a graded evaluation plan. We've also added a module on failure-mode analysis — the skill of predicting how your LLM feature embarrasses itself before your users find out.
Five hundred students taught us more than we taught them. We're grateful, and our curriculum is better for it.
