A Study of Prompt Engineering Techniques for Improving Generative AI Performance

Generative Artificial Intelligence (GenAI) has transformed the way users create content, retrieve information, write code and solve problems. The quality of outputs produced by Generative AI systems depends not only on the underlying model but also on the instructions supplied by users. Prompt engineering is the process of designing, structuring and refining inputs to guide an AI model towards relevant, accurate and useful responses. The present study examines the role of prompt engineering techniques in improving the performance of Generative AI systems, with particular attention to Zero-Shot Prompting, One-Shot and Few-Shot Prompting, Role-Based Prompting, Contextual Prompting and Chain-of-Thought Prompting. The study adopts a descriptive research design and is qualitative in nature, relying on secondary data collected from academic research papers, conference proceedings and published surveys. The literature indicates that carefully structured prompts can improve task alignment, response relevance, reasoning performance and output consistency. However, prompt effectiveness varies according to the task, model, context and evaluation method. The study also identifies limitations such as hallucinations, sensitivity to wording, prompt injection risks, bias and the absence of universally effective prompt templates. It concludes that prompt engineering is an important method for obtaining more reliable results from Generative AI, but should be combined with verification, clear evaluation criteria and responsible AI practices.