AI can draft code in seconds. The hard part is deciding what to ask, how much context to provide, and whether the answer survives a real test. If you have ever accepted a plausible AI patch and later found a regression, this course gives you a practical engineering workflow for doing better. You will work through a small, synthetic Python web service called RelayDesk. The course shows the actual requests made to AI assistants and coding agents, edited into readable evidence walkthroughs. You will see a duplicate-event bug, a first green patch that still misses a database invariant, a bounded feature request, an old-schema failure, a review-found boundary bug, and an evidence-backed capstone handoff. These are local educational exercises, not claims about a production service or a human typing live on screen.
Here is what you will practice:
- Turn vague requests into prompts with a clear task, relevant context, constraints, and verification criteria.
- Give an agent enough repository evidence without dumping unrelated files; measure usage and avoid confusing session token counts with an API bill.
- Read coding benchmark methodology, identify what a leaderboard cannot prove, and run a fair local comparison on your own task.
- Limit an agent’s authority, inspect its changes, reproduce red tests, and independently verify the result.
- Find flaws that a green test suite misses: database uniqueness, old schemas, data exposure, pagination, retries, and boundary behavior.
- Write a runbook and a release decision that distinguish a locally working teaching reference from a production-ready change.
