Why Short AI Coding Prompts Can Cost You More Time

Ever tried to save time by keeping a prompt short - only to spend the next ten minutes answering follow-up questions because the AI didn't have what it needed?Yeah, me too.I recently came across a really interesting post from the GitHub Copilot team about making AI coding more cost efficient without sacrificing task quality. One of the ideas that stuck with me was something they called the "local metric trap."The basic idea is simple: you optimise the bit you can see - one response or tool call - but accidentally make the whole task slower and more expensive.GitHub found this while testing ways to compress tool output. When useful details were removed, the agent would reopen the original output or rerun the command to find them again. The first response was shorter, but all that recovery work ended up costing more.GitHub was looking at the machinery inside its agent harness - not studying how developers write prompts. Weirdly, I recognised the same pattern in how I use coding…