See-Saw Generative Mechanism for Scalable Recursive Code Generation with Generative AI

TL;DR

This paper presents the See-Saw generative mechanism, a novel recursive approach for scalable code generation with AI that manages dependencies and token limitations effectively.

Contribution

It introduces a dynamic, recursive code generation method that alternates between main code and dependencies to improve scalability and coherence.

Findings

Successfully manages hundreds of interdependent files

Maintains code coherence and functionality

Reduces computational overhead

Abstract

The generation of complex, large-scale code projects using generative AI models presents challenges due to token limitations, dependency management, and iterative refinement requirements. This paper introduces the See-Saw generative mechanism, a novel methodology for dynamic and recursive code generation. The proposed approach alternates between main code updates and dependency generation to ensure alignment and functionality. By dynamically optimizing token usage and incorporating key elements of the main code into the generation of dependencies, the method enables efficient and scalable code generation for projects requiring hundreds of interdependent files. The mechanism ensures that all code components are synchronized and functional, enabling scalable and efficient project generation. Experimental validation demonstrates the method's capability to manage dependencies effectively while maintaining coherence and minimizing computational overhead.