Generative AI Toolkit -- a framework for increasing the quality of LLM-based applications over their whole life cycle

Since their introduction LLM have gained widespread traction in different domains. They can be used as stand-alone products, but also to augment existing software products such as applications (also called agentic functions) or machine learning agents (also called LLM-based agents) to increase their capabilities. In this section, we show challenges during development and operation of LLM-based applications on three examples. Users interact with LLM-based applications by entering input into the LLM, the so-called prompt. Jang et al. showed in 2023 that the LLM's output is very sensitive to variations of the prompt [1]. Thus, the task of finding the best prompt to generate expected or best output leads to manual, trial-and-error-prompt experimenting - a method well known as prompt-engineering (cf. White et al. in 2023 for ChatGPT [2] or a survey of prompt techniques by Schulhoff et al. in 2024 [3]). Additionally, the outputs of an LLM-based application can not only vary, but also be wrong without telling a user ("hallucination", cf.