This article explores how to achieve complex software development functionality given the limited context length of Large Language Models (LLMs). The author shares practical insights: LLM context is insufficient and expanding it leads to performance degradation, making it difficult to handle multi-logic planning and debugging issues. The solution suggests that developers first conduct their own research, then use AI to generate initial design plans, and continuously iterate and modify the code to refine it. The article provides specific prompt examples, such as analyzing file errors or refactoring code, and emphasizes manual review of plans, discussion, and design optimization at each step, proceeding to functional testing only after basic implementation. This strategy helps developers effectively overcome LLM limitations and improve development efficiency, particularly suitable for AI-assisted programming scenarios.
