Is English the New Programming Language? How About Pseudo-code Engineering?

Resumo

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 research is motivated by the intricacies of human-computer interaction within the context of conversational AI, focusing on the role of structured inputs from pseudo-code engineering in enhancing chatbot comprehension and response accuracy. Objectives: This study investigates the comparative effectiveness of natural language versus pseudo-code engineering generated inputs in eliciting precise and actionable responses from ChatGPT, a leading language model by OpenAI. It aims to delineate how different input forms impact the model's 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, advocating for its broader application across disciplines requiring precise AI responses. It highlights pseudo-code engineering's efficacy in achieving more deterministic, concise, and direct outcomes from AI systems like ChatGPT, pointing towards future innovations in conversational AI technology.