The field of hardware design automation is witnessing significant advancements with the integration of large language models (LLMs). A key direction in this area is the development of innovative frameworks that leverage LLMs to generate high-quality Register-Transfer Level (RTL) code and hardware description languages (HDLs). These frameworks aim to address the challenges of syntax errors, functional hallucinations, and weak alignment to designer intent. Notably, researchers are exploring the use of reinforcement learning, formal verification, and high-level synthesis to improve the reliability and correctness of LLM-generated designs. One of the noteworthy papers in this area is EARL, which presents an entropy-aware reinforcement learning framework for Verilog generation. Another notable paper is ProofWright, which introduces an agentic verification framework that integrates automated formal verification with LLM-based code generation. The CorrectHDL framework is also noteworthy, as it leverages high-level synthesis results to correct potential errors in LLM-generated HDL designs.