‘Talk like a caveman’ prompts save tokens, but far less than promised
Developers looking to curb the cost of AI-powered coding tools have increasingly turned to the “Caveman” prompting style, which instructs coding assistants to communicate in blunt, telegraphic language and avoid conversational padding. The theory is simple: fewer words mean fewer tokens, translating into lower inference costs for organizations deploying AI agents at scale. A new test from IDE maker JetBrains confirms that terse prompting styles such as the viral open-source Caveman project can reduce token usage without hurting coding performance. However, the company found that the savings were far smaller than supporters claim. JetBrains used the Harbor open-source evaluation framework and tasks from SkillsBench for its test, and found that the Caveman technique reduced usage of output tokens by about 8.5%, far below its claimed 65%. The IDE-maker ran paired benchmarks across 86 real-world software engineering tasks in Claude Code, comparing coding sessions that used the Caveman pro