Secondly, accurately structuring the input is crucial as LLMs fundamentally depend on the prompt. Input with patterns that are not well-reflected in the training dataset of the model leads to poor performance. Controlling the output of LLMs as a function of their inputs to avoid producing incorrect, harmful, or biased content is an active field of study in computer science literature . A common approach to address this is known as prompt engineering , which essentially involves crafting the input with robust logic and adapting it to the specificities of a models architecture and training dataset.