Recent experiments have focused on mitigating AI bias through prompt engineering, specifically analyzing how different AI prompt designs influence the generation of unbiased and fair content from large language models (LLMs). An experimental methodology was used to derive key insights from experiments conducted with OpenAI’s GPT-4o and Anthropic’s Claude-3.5 models, highlighting AI cognitive dissonance when handling conflicting instructions in user prompts. Prompt engineering, defined as the iterative process of developing a prompt by modifying or changing it, plays a crucial role in guiding the output of generative AI models. The first Prompt Engineering Guide, published in October 2022 by Learn Prompting, has been widely cited by major entities including Google, Wikipedia, and the US Government's National Institute of Standards and Technology (NIST).