BetterV's techniques can be extended to other hardware description languages like VHDL by adapting the data processing and instruct-tuning stages to accommodate the syntax and semantics of VHDL. The process of collecting, filtering, and processing datasets can be tailored to include VHDL code repositories. Instruct-tuning methods can be modified to teach the LLMs the specific knowledge and syntax of VHDL, similar to how it was done for Verilog. Additionally, data augmentation techniques can be applied to enrich the training set with VHDL-specific variations. The generative discriminator can be trained on VHDL-related downstream tasks to guide the LLMs in generating syntactically and functionally correct VHDL code.