  Generating diverse, high-quality outputs from language models is crucial for
applications in education and content creation. Achieving true randomness and
avoiding repetition remains a significant challenge. This study uses the Linear
Congruential Generator method for systematic fact selection, combined with
AI-powered content generation. We ensured unique combinations of
gastrointestinal physiology and pathology facts across multiple rounds,
integrating these facts into prompts for GPT-4o to create clinically relevant,
vignette-style outputs. Over 14 rounds, 98 unique outputs were generated,
demonstrating LCG's effectiveness in producing diverse and high-quality
content. This method addresses key issues of randomness and repetition,
enhancing the quality and efficiency of language model-generated content for
various applications.
