AI coding assistants are now a daily tool, but most teams use them unstructured, i.e., pasting whole repos, leaving giant chats running for days, and reflexively reaching for the most expensive model. The result is slow responses, blown token or premium-request budgets, and counterintuitively, worse code, because models lose accuracy as their context fills up ("context rot").

This session is a practical guide to using AI assistants with discipline. I'll cover the eight levers that actually move the needle: context engineering, prompt caching, tool design, custom instructions and skills, model routing, output discipline, repo hygiene, and workflow patterns including the Ralph Wiggum loop, auto-compact, and agent handover. Live demos and a token-cost calculator will show the impact in real numbers. You'll leave with a checklist your team can apply this week.