AI Coding Efficiency: Optimize Token Usage and Cost

AI coding tools can read too much, carry stale context, repeat searches, return oversized answers, and consume budget without producing an accepted change. Shorter prompts alone do not solve that problem.

This course teaches a repeatable, vendor-neutral workflow for optimizing AI-assisted software development. Every technique is demonstrated with the concrete developer action, the reason it works, and the measurement boundary needed to avoid unsupported savings claims.

You will learn how to begin each task with clean context, define scope before repository search, search broadly but read narrowly, exclude generated noise, build a small repository map, write complete task and output contracts, reduce logs deterministically, route work by risk, and measure cost per accepted task.

The practical lab generates an AI-ready Java project from a reusable Maven archetype. You will inspect the generated .ai folder, understand what each file contributes, remove unnecessary guidance, and set up a controlled baseline-versus-configured experiment.

The course applies across AI assistants, IDE integrations, command-line tools, code-review tools, and automated agents. It does not promise that fewer local repository bytes automatically equal lower provider billing. Instead, it shows you how to record actual tokens, elapsed time, retries, changed files, verification, reviewer effort, and cost.

By the end, you will have a practical operating model and reusable starter project for making AI development work more focused, measurable, and economically defensible.