The way individuals approach activities in the fields of education, professional work, and creativity has altered dramatically as a result of the widespread adoption of large language models (LLMs) like ChatGPT, Gemini, and DeepSeek. This study examines the effects of user prompt structure and clarity on the efficiency and efficacy of LLM outputs. We examine AI usage patterns, prompting techniques, and user satisfaction using information from 243 survey participants with a range of educational and professional backgrounds. The findings demonstrate that users report greater work efficiency and better results when they use prompts that are explicit, structured, and context-aware. These results highlight how crucial prompt engineering is to optimizing the benefits of generative AI and offer useful ramifications for its daily application.