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Working with AI (Part 2): Code Conversion

Jul 21, 2024 - news.bensbites.com
The article discusses how Mantle, a software development company, used Language Learning Models (LLMs) to convert a prototype project into a production project. The prototype was written in R and needed to be converted to Golang and ReactJS. The team used Gemini’s LLMs to reduce the scope of the project by two-thirds, saving significant developer time. They used the LLMs to translate the R code using their production code patterns, file structures, and libraries, aiming to get 80% of the boilerplate and repeated patterns out of the way so that engineers could focus on the 20% – high-value polish to ship the project to customers.

The team used a step-by-step process to generate code, starting with gathering context, inputting prototype source code, deriving existing code patterns, and adding existing libraries. They also used screenshots as visual reference points and reinforced the process with already generated or ported code. They generated files one by one, starting from backend to frontend and from leaf node files to more common and connected ones. The team also dealt with large files by generating complete segments of files that they could concatenate together in their context. The article concludes by stating that as token windows continue to grow and models become more adept at understanding and generating code, there will be even greater efficiencies and improved code quality in the conversion process.

Key takeaways:

  • Mantle utilized Large Language Models (LLMs) to streamline the process of converting a prototype project into a production project, reducing the scope by two-thirds and saving significant developer time.
  • The team used Gemini LLMs and their >1 million token context windows to generate large scale codebases in their style and structure, connected to common utilities.
  • The process involved gathering context, inputting prototype source code, deriving existing code patterns, using existing libraries, using screenshots as visual reference points, and reinforcing with already generated or ported code.
  • As token windows continue to grow and models become more adept at understanding and generating code, there will be even greater efficiencies and improved code quality in the conversion process, leading to more rapid and cost-effective software development.
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