This research introduces Uncertainty-Guided Chain-of-Thought (UG-CoT) to optimize how LLMs generate code by intelligently adapting reasoning depth based on task complexity.

• Tackles the problem of LLMs 'overthinking' simple coding tasks
• Dynamically adjusts reasoning depth using uncertainty signals
• Achieves both higher accuracy and computational efficiency
• Demonstrates up to 36% reduction in token consumption without sacrificing performance

For engineering teams, this approach means more reliable code generation with fewer computational resources, addressing a key challenge in practical AI deployment for software development.

Uncertainty-Guided Chain-of-Thought for Code Generation with LLMs