The field of large language models (LLMs) is rapidly advancing, with significant developments in clinical and software applications. Researchers are exploring the use of LLMs in clinical note generation, with studies investigating their reliability and consistency. The results show that LLMs can generate high-quality clinical notes, but their performance may vary depending on the specific model and task. In software engineering, LLMs are being used to automate requirements generation and release note generation, with promising results. However, more work is needed to refine these systems and ensure their accuracy and reliability. Noteworthy papers in this area include Assessing the Quality of AI-Generated Clinical Notes, which developed a framework to evaluate the quality of LLM-generated clinical notes, and ReqBrain, which introduced a task-specific instruction tuning method for LLMs in requirements generation. Another notable paper is SmartNote, which proposed a personalized release note generator using LLMs. Overall, the field is moving towards increased adoption of LLMs in clinical and software applications, with a focus on improving their performance, reliability, and usability.