Using Gemini Pro to Streamline Code Conversion at Mantle

When it comes to software development, converting a prototype into a production-ready codebase is often a daunting task. At Mantle, we faced this challenge head-on with a novel approach that leveraged the latest advancements in AI, specifically Google’s Gemini 1.0 Pro. This LLM (Large Language Model) helped us reduce project scope by two-thirds and saved months of developer time.

The Challenge

In software development, converting a codebase from one language to another is a common but complex task. Here are the main reasons why organizations undertake such initiatives:

• Improved Maintainability: Legacy codebases can become cumbersome and hard to maintain, especially if the original developers have moved on. Switching to a more widely used language can make the code easier for current and future developers to understand and modify.
• Performance Boost: Different languages excel in different areas. If an application is struggling with performance benchmarks, migrating to a faster or more efficient language can provide significant improvements.
• Talent Pool: Finding skilled developers for a specific language can be challenging. Migrating to a more common language can help attract and retain talent.
• Productizing Prototypes: Prototypes are often written in languages or frameworks that are quick and easy to use, but not suitable for production environments.

Despite the clear benefits, these projects are notoriously time-consuming and fraught with risks. Instead of advancing customer-facing features, developers must spend valuable time recreating existing functionality. Project timelines often exceed estimates due to unforeseen issues and complexities.

Mantle’s Approach

At Mantle, we had a prototype written in R that needed to be converted to our standard production tech stack: Golang for the backend and ReactJS for the frontend. Our goal was to use an LLM to translate the R code into Golang and ReactJS while maintaining the original logic and intent.

Why Gemini 1.0 Pro?

Gemini 1.0 Pro, with its one million token window, offered a powerful tool for this task. Here’s how we leveraged it:

• Large Token Window: The one million token limit allowed us to feed large chunks of code into the model, ensuring context and consistency.
• Custom Prompts: We crafted custom prompts to guide the LLM in understanding our specific production patterns, file structures, and libraries.
• Iterative Refinement: The process involved multiple rounds of refinement. Initial outputs were reviewed, and feedback was used to improve subsequent translations.

Implementation Details

• Data Preparation: We prepared a dataset of R code snippets along with their corresponding Golang and ReactJS equivalents. This dataset helped train the LLM on our specific requirements.
• Model Training: While Gemini 1.0 Pro is pre-trained, we fine-tuned it using our custom dataset to improve accuracy and relevance.
• Code Translation: The LLM translated R code into Golang and ReactJS, maintaining the original logic and adhering to our production standards.
• Review and Refinement: A team of developers reviewed the translated code for accuracy, performance, and adherence to best practices. Feedback was used to refine the model further.

Results

Using Gemini 1.0 Pro, we achieved significant results:

• Reduced Scope by Two-Thirds: The LLM helped us convert a large portion of the prototype code, reducing the overall project scope.
• Saved Months of Developer Time: By automating much of the translation process, we saved valuable developer time that could be redirected to other high-priority tasks.
• Improved Code Quality: The translated code was reviewed and refined by experienced developers, ensuring it met our production standards.

Conclusion

At Mantle, leveraging AI through Gemini 1.0 Pro has proven to be a game-changer in streamlining the process of converting prototypes into production-ready codebases. By reducing project scope and saving developer time, we were able to focus on advancing customer-facing features while maintaining high code quality.