Tired of writing tedious README files for your code repositories? Imagine a tool that could automatically generate the basic documentation for you, freeing you to focus on the code itself. Researchers are exploring how Large Language Models (LLMs) can do just that, creating a helpful assistant for developers. This new approach uses a fine-tuning technique, allowing the LLM to learn from existing README files and generate new ones based on the contents of a given repository. The application indexes the codebase, allowing the LLM to understand the project's structure and functionality. It then uses clever prompt engineering to guide the LLM in creating a README file with standard sections like description, installation, usage, and contribution guidelines. This isn't just about saving time; it's about making open-source code more accessible. Many public repositories lack basic documentation, making it difficult for others to understand and contribute. This tool aims to bridge that gap, fostering collaboration and improving the overall quality of open-source projects. While the initial results are promising, there are challenges. The generated README files sometimes require editing, and the reliance on LLMs introduces the risk of inaccuracies. However, this is a significant step toward automating a time-consuming task, offering a valuable tool for developers and potentially revolutionizing how we document and share code. The researchers have even made their tool open-source and customizable, allowing developers to fine-tune it on their own datasets and potentially improve its accuracy and tailor it to specific needs. This opens exciting possibilities for future development, including integration with popular code editors and support for multiple programming languages. As AI continues to evolve, we can expect even more sophisticated tools that empower developers and streamline the software development process.