RustGen: An Augmentation Approach for Generating Compilable Rust Code with Large Language Models

Foundation models show an impressive ability to write code snippets. However, there are still challenges when generating code for resource-poor programming languages. In this work, using Rust as an example, we tackle these challenges through in-context learning, with additional components that feed back compile errors to the LLM until it converges on a runnable code that is free of several common programming errors. We describe the specific techniques that allow us to do this -- history-based search, prompt engineering, and syntax-based skeletonization -- and evaluate their benefits on a set of code generation tasks in Rust.