LMM chaining with the output generations becoming the input of the next step has been shown to not only have "improved the quality of task outcomes, but also significantly enhanced system transparency, controllability, and sense of collaboration" according to Wu, T., et al. in 2022 . For more complex prompts, it was found that developing an interactive interface called PromptChainer for visually programming chains supported transforming data between steps of a chain, as well as debugging the chain . Chaining LMM outputs together has become a large undertaking by many libraries, and has become a standard for platforms such as LangChain and CrewAI . AI chain engineering techniques in 2024 were developed by Cheng, Yu., et al. through systematic reviews, summarising, and refinement. The authors also developed a tool called Prompt Sapper "which embodies these AI chain engineering principles and patterns naturally in the process of building AI chains, thereby improving the performance and quality of AI chains." In addition, Vashishtha, S., et al. in 2024 utilized a large language model (LLM) to systematically extract attributes from item meta-information, which were then passed to a textto-image model via prompt engineering to generate images .