  The Rust programming language, with its safety guarantees, has established
itself as a viable choice for low-level systems programming language over the
traditional, unsafe alternatives like C/C++. These guarantees come from a
strong ownership-based type system, as well as primitive support for features
like closures, pattern matching, etc., that make the code more concise and
amenable to reasoning. These unique Rust features also pose a steep learning
curve for programmers.
  This paper presents a tool called RustAssistant that leverages the emergent
capabilities of Large Language Models (LLMs) to automatically suggest fixes for
Rust compilation errors. RustAssistant uses a careful combination of prompting
techniques as well as iteration with an LLM to deliver high accuracy of fixes.
RustAssistant is able to achieve an impressive peak accuracy of roughly 74% on
real-world compilation errors in popular open-source Rust repositories. We plan
to release our dataset of Rust compilation errors to enable further research.
