Prompt Engineering Trick Reduces Excessive LLM Reasoning Loops
A user shared an additive prompt technique designed to prevent local large language models (LLMs) from getting stuck in endless, repetitive reasoning loops. The method encourages models to adopt an iterative, fractal approach of building a rough draft and refining it step-by-step rather than planning excessively. This technique offers a practical workaround for users experiencing efficiency bottlenecks and overthinking in smaller local AI models. By curbing unnecessary reasoning loops, users can achieve faster and more confident text or code generation. The prompt instructs the LLM to use a fractal construction procedure where it starts with a basic draft, tests or reviews it, and progressively adds details to specific portions. The author subsequently refined the prompt to focus on smaller portions at a time to prevent the model from becoming overwhelmed by broad artistic pressure.
## BACKGROUND
Local LLMs are artificial intelligence models downloaded and run directly on a user's local hardware rather than accessed via cloud APIs. When prompted, these models sometimes suffer from over-planning or getting trapped in prolonged reasoning cycles where they repeatedly evaluate internal plans without making actual progress.