Enhancing LLM-Generated Hardware Documentation: Post-Processing and Prompt Engineering Techniques

Abstract:
Adopting Large Language Models (LLMs) has recently gained prominence in various natural language processing tasks, including in the electronic design automation industry. As an inverse approach, this work considers using LLMs to process formal hardware models and generate human-readable design documentation hereof. We automatically preprocess formalized system-level hardware specifications to create prompts for LLMs. Based on these prompts, an LLM generates an extensive, human-readable explanation of the system. While this workflow has already shown to be viable as a concept, technical errors and style issues are the prevalent restrictions for wide-scale application. Addressing these issues, this paper presents a selection of advanced post-processing and prompt engineering techniques to improve the quality of the LLM-generated documentation. Applying our extended workflow to a set of hardware components demonstrates that the incorporated methods are especially effective in ensuring the documentation’s correct formatting and style conformity. Although occasional technical errors still occur, we observe a significant reduction in manual revision efforts, with 46.4% of the generated documentation not requiring any further changes.