AI-Assisted Software Development: From Vibe Coding to Reliable Products

A coding agent makes everyone faster on day one. Then the bill arrives — a 47-file pull request you can't defend, a migration that ate yesterday's data, a key checked into git, a feature that ships green tests and a broken flow, a codebase you can't navigate three weeks later. The 2025 evidence is unflattering and consistent: in a controlled study experienced developers were 19% *slower* with AI assistance while believing they had sped up; nearly half of AI-generated code samples carried OWASP Top-10 vulnerabilities; short-term churn nearly doubled and duplicate code grew by an order of magnitude. The tools are not the problem. The missing discipline is. This course is that discipline. Across 34 lessons you'll learn the one core loop — plan, implement, verify against an external check — and the things that surround it: engineering the context an agent actually needs, writing a prompt and a spec that produce a clean diff first time, sandboxing the worst plausible mistake, tests that actually fail when the feature is wrong, reading an AI-generated diff like a senior engineer, and where to *stop* — the small zone (auth, secrets, payments, migrations) where you do the work yourself. A whole phase is dedicated to the risk classes AI-generated code quietly produces: the vulnerability patterns from the studies above, hallucinated packages and slopsquatting, prompt injection and excessive agency, the architectural drift that turns last quarter's prototype into next quarter's rewrite. Tool names will change; the categories and trade-offs won't, and the course is built on the durable layer. You'll leave with a habit for picking the right workflow per task, and a codebase you'd put your name on.