  Background: The integration of artificial intelligence (AI) into daily life,
particularly through chatbots utilizing natural language processing (NLP),
presents both revolutionary potential and unique challenges. This intended to
investigate how different input forms impact ChatGPT, a leading language model
by OpenAI, performance in understanding and executing complex, multi-intention
tasks. Design: Employing a case study methodology supplemented by discourse
analysis, the research analyzes ChatGPT's responses to inputs varying from
natural language to pseudo-code engineering. The study specifically examines
the model's proficiency across four categories: understanding of intentions,
interpretability, completeness, and creativity. Setting and Participants: As a
theoretical exploration of AI interaction, this study focuses on the analysis
of structured and unstructured inputs processed by ChatGPT, without direct
human participants. Data collection and analysis: The research utilizes
synthetic case scenarios, including the organization of a "weekly meal plan"
and a "shopping list," to assess ChatGPT's response to prompts in both natural
language and pseudo-code engineering. The analysis is grounded in the
identification of patterns, contradictions, and unique response elements across
different input formats. Results: Findings reveal that pseudo-code engineering
inputs significantly enhance the clarity and determinism of ChatGPT's
responses, reducing ambiguity inherent in natural language. Enhanced natural
language, structured through prompt engineering techniques, similarly improves
the model's interpretability and creativity. Conclusions: The study underscores
the potential of pseudo-code engineering in refining human-AI interaction and
achieving more deterministic, concise, and direct outcomes, advocating for its
broader application across disciplines requiring precise AI responses.
