As AI coding tools and agents become standard in developer workflows, token costs are emerging as a significant production challenge. This course equips you with practical techniques for reducing LLM token usage without sacrificing the accuracy or reliability of your AI outputs. Explore strategies including context compression, smart summarization, memory management, and selective context retention—the same approaches used by engineers building efficient, scalable AI systems. Whether you work with tools like Cursor, Copilot, or Claude Code, or build your own agents and RAG pipelines, these techniques apply across the stack. By the end of this course, you’ll be equipped with 10 actionable optimization methods you can apply to reduce costs and improve performance.
This course is integrated with GitHub Codespaces, an instant cloud development environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace.
