The One-Line Prompt That Cut Token Usage by 37.91% (modgo.org)

A recent experiment demonstrates that restructuring source code using a targeted one-line prompt can reduce AI token consumption by nearly 38%, significantly improving efficiency in AI-assisted coding. By refactoring a gpt-5–generated codebase to employ design patterns like Strategy and Factory, and splitting the implementation into separate files, the researcher observed a consistent decrease in token usage across multiple runs and models—including gpt-5 and claude-4-sonnet—while maintaining correct functionality in every single-shot prompt execution. This finding is significant for the AI/ML community as it highlights how well-organized, modular code not only enhances human readability but also enables AI models to generate outputs more efficiently, with lower computational and financial costs. The experiment’s thorough setup ensured fairness by alternating between the original and refactored codebases under zero-context conditions and repeating feature-addition prompts. Token savings were substantial, sometimes approaching a 2× reduction with claude-4-sonnet, emphasizing that prompt engineering combined with thoughtful code structure can optimize AI workflows in software development. Technically, the key prompt that transformed the code instructed the AI to apply design patterns and reorganize files, demonstrating how “vibe coding” with AI benefits from explicit, high-level structural guidance. Since the full experiment, source code, and prompts are publicly available, this work offers a reproducible benchmark and practical insight for developers aiming to harness AI’s full potential while managing token budgets effectively.