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 prompting style against otherwise identical sessions without it.
While an initial evaluation of just 10 tasks indicated savings to the tune of about 30%, the reduction fell to about 8.5% as the test progressed, JetBrains engineer Denis Shiryaev, wrote in a blog post, suggesting that the Caveman technique’s impact was less pronounced across a broader and more representative workload.
