The skill is steering the model, not prompting it
AI-assisted coding has a trap built in: going reactive. You start chasing the model – racing to keep up with whatever it just generated, waving through code because it arrived fast and looks plausible. The skill is the opposite. It is staying firmly in control of the three things that actually matter: how fast you implement, how good the result is, and whether it still answers the requirement you started from. You direct the model rather than run after it. That control is a learnable craft – giving the model the right context, knowing when to let it run and when to rein it in, reading generated code fast enough to catch the plausible-but-wrong before it reaches a merge – and this category is the hands-on track for building it, across the four tools the market has settled on.
The training can be used regardless of your tool of choice: Claude Code, Cursor, GitHub Copilot, or JetBrains Junie, each from a one-day fast-track to a three-day applied workshop. The deep-dives go past the demo into context control, review habits, and the failure modes nobody shows you in the keynote. TDD with AI sits here too, because the test is how you keep generated code honest at the keyboard.
This track is the keyboard itself – the craft of steering the model and staying in control of speed and correctness at the same time. The layer above it – methods, architecture, the disciplines that turn fast generation into reliable delivery – is the AI Implementation track. Fluency transfers between tools, so most people who start with one come back for a second; the AI-Assisted Software Development path sequences both layers in order.