Imagine writing code once and having it run smoothly on both your computer and a custom-built chip. That's the dream of High-Level Synthesis (HLS), a technology that translates C/C++ code into hardware designs. However, getting regular C/C++ code ready for HLS is a major headache, demanding tedious manual tweaks to make it "hardware-friendly." Researchers are exploring whether Large Language Models (LLMs), known for their coding prowess, can automate this tricky process. The challenge? LLMs sometimes hallucinate, generating code that looks right but fails in practice, especially when it comes to the nuances of hardware. A new approach uses a "Retrieval-Augmented Generation" (RAG) technique. Imagine the LLM having access to a library of proven code fixes. With RAG, the LLM can search this library for similar problems and use the solutions as a guide, dramatically improving its accuracy. Furthermore, these LLMs can even analyze your code and input data to determine the most efficient bit widths for variables, leading to smaller, faster, and less power-hungry circuits. Early results are promising, showing that LLM-guided repair can significantly boost the success rate of HLS code generation, automating a previously laborious and error-prone task. While still under development, this research suggests that LLMs could revolutionize how we design hardware, making it easier and faster to create customized chips for a wide range of applications.