What you'll learn

GitHub Copilot's shift to usage-based AI Credits means every model choice, every token of context, and every verbose response now has a direct cost, and developers who keep working the way they did under flat-rate plans are quietly burning their own or their team's budget. In this course, Cost-optimized Engineering with GitHub Copilot: Architecting for AI Credits and Multi-model Selection, you'll gain the ability to deliver the same engineering output at a fraction of the credit spend. First, you'll explore the economics of AI Credits and learn to route every task to the most cost-efficient model tier, configuring persistent model routing in VS Code and verifying real spend in the AI usage dashboard. Next, you'll discover how to craft surgical context with explicit file references, keep Copilot's prompt cache alive through disciplined sessions, and write custom instructions that force dense, code-only outputs. Finally, you'll learn how to run agentic workflows economically with Plan mode, delegate well-specified tasks to the coding agent under session credit limits, and audit inefficient agentic sessions to eliminate context drift and cyclical token consumption. When you're finished with this course, you'll have the skills and knowledge of cost-optimized GitHub Copilot engineering needed to architect AI-assisted workflows that maximize value per credit for you and your team.