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It covers foundational prompt design principles and approaches such as explicit constraints, iterative refinement, and prompt chaining, while addressing the balance among user intent, system behavior, and ethical considerations. The article also discusses integrating external resources and APIs, advanced controls like temperature and token limits, handling ambiguous inputs, and demonstrates real-world case studies across technical support, content generation, retrieval, and STEM education.",{"@graph":69,"@context":121},[70,84,104],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/prompt-engineering-for-chatgpt-a-quick-guide-to-techniques-tips-and-best-practices/672928/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":98,"encodingFormat":97,"isAccessibleForFree":99,"interactionStatistic":100},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/prompt-engineering-for-chatgpt-a-quick-guide-to-techniques-tips-and-best-practices/672928.png","ImageObject",300,407,{"name":92,"@type":93},"asda1da","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-02",true,{"@type":101,"interactionType":102,"userInteractionCount":8},"InteractionCounter",{"@type":103},"ViewAction",{"@type":105,"mainEntity":106},"FAQPage",[107,113,117],{"name":108,"@type":109,"acceptedAnswer":110},"What is prompt engineering in the context of ChatGPT?","Question",{"text":111,"@type":112},"Prompt engineering is the practice of crafting effective prompts that guide ChatGPT toward the desired responses, improving the quality and relevance of outputs by aligning user intent with model understanding.","Answer",{"name":114,"@type":109,"acceptedAnswer":115},"How do explicit constraints and iterative refinement help produce better results?",{"text":116,"@type":112},"They clarify expectations for the model and allow successive improvements to the prompt, reducing ambiguity and steering the output toward more accurate, focused responses.",{"name":118,"@type":109,"acceptedAnswer":119},"What advanced strategies are discussed for controlling output quality?",{"text":120,"@type":112},"The article addresses controlling parameters such as temperature and token limits, adapting prompts to specific domains, and managing ambiguous or incomplete user inputs.","https://schema.org",{"og:url":83,"og:type":123,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":125,"canonical":83},"index,follow",{"doc_id":127,"site_id":62},672928,1793615324,{"code":4,"msg":5,"data":130},{"doc_id":127,"user_id":131,"nickname":92,"user_avatar":132,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":8,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":138,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":128,"read_time":143},3985747861852,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","| Prompt Engineering for ChatGPT: A Quick Guide to Techniques, Tips, and Best Practices |  |  |\n| --- | --- | --- |\n| Sabit Ekin\u003Cbr>DOI: 10.18260/jet.43.1.5 |  |  |\n| \u003Cbr>In the rapidly evolving landscape of artificial intelligence (AI), generative language models such as ChatGPT (Generative Pre-trained Transformer) are increasingly being adopted across engineering and technology-focused disciplines. To effectively leverage these tools, users must understand prompt engineering—the practice of designing and refining inputs to guide AI systems toward useful and reliable outputs. This article provides a comprehensive guide to prompt engineering techniques and best practices, with an emphasis on applied use cases relevant to achieving effective outcomes with ChatGPT. The discussion introduces foundational prompt design principles, explores techniques such as explicit constraints, iterative refinement, and prompt chaining, and examines strategies for balancing user intent, system behavior, and ethical considerations. The article also discusses the integration of external resources and APIs (Application Programming Interface), along with practical considerations for responsible and ethical use of generative AI systems. Advanced strategies, such as temperature and token control, domain-specific adaptation, and handling ambiguous or incomplete inputs, are also addressed. Real-world case studies demonstrate the practical applications of prompt engineering in technical support, content generation, domain-specific knowledge retrieval, and engineering technology education. The article concludes by highlighting prompt engineering as an emerging technical skill that can enhance engineering technology instruction, support workforce readiness, and promote responsible use of generative AI tools.\u003Cbr>1. Introduction\u003Cbr>1.1 Brief overview of ChatGPT\u003Cbr>ChatGPT (OpenAI 2023a) is an advanced language model developed by OpenAI, based on the GPT-4 architecture. It has been trained on a large corpus of text and has the ability to generate humanlike responses in various contexts (including generating images using Deep Learning methods) . ChatGPT’s primary strength lies in its ability to understand and generate text in a wide range of domains, making it suitable for applications such as customer support, |  | content generation, and interactive storytelling. This capability builds on foundational work in large language models (LLMs), notably the few-shot learning abilities demonstrated by GPT-3 (Brown et al. 2020), the instruction-tuning innovations via human feedback (Ouyang et al. 2022), and the expanded capabilities of GPT-4 (OpenAI 2023b) .\u003Cbr>The rapid integration of generative AI tools into professional workflows has also intensified discussions surrounding workforce readiness, digital literacy, and the need for AI-augmented skill development in engineering and technology disciplines (Di Battista et al. 2023; Sciences et al. 2018; Bommasani et al. 2021; Kasneci et al. 2023) .\u003Cbr>Recent studies have begun examining the integration of generative AI tools within engineering and STEM classrooms, highlighting both instructional opportunities and challenges related to skill development, academic integrity, and workforce preparedness (Cotton, Cotton, and Shipway 2024; Kshetri et al. 2023; Kasneci et al. 2023) .\u003Cbr>1.2 Importance of prompt engineering in maximizing the effectiveness of ChatGPT\u003Cbr>Prompt engineering is the art of crafting effective prompts that guide ChatGPT to generate desired responses. It plays a crucial role in maximizing the effectiveness of the language model by bridging the gap between user intent and model understanding. A well-engineered prompt can significantly improve the quality and relevance of ChatGPT’s output, while a poorly crafted one may lead to unsatisfactory or erroneous responses.\u003Cbr>For example, consider the following prompts and their corresponding ChatGPT responses:\u003Cbr>Prompt 1: “Tell me about AI.”\u003Cbr>Response 1: “A","cbCairupEtQHEMhV","https://ap.wps.com/l/cbCairupEtQHEMhV","pdf",839920,17,"English","# Introduction\n## Brief overview of ChatGPT\n## Importance of prompt engineering in maximizing the effectiveness of ChatGPT\n## Objective and structure of the article","[{\"question\":\"What is prompt engineering in the context of ChatGPT?\",\"answer\":\"Prompt engineering is the practice of crafting effective prompts that guide ChatGPT toward the desired responses, improving the quality and relevance of outputs by aligning user intent with model understanding.\"},{\"question\":\"How do explicit constraints and iterative refinement help produce better results?\",\"answer\":\"They clarify expectations for the model and allow successive improvements to the prompt, reducing ambiguity and steering the output toward more accurate, focused responses.\"},{\"question\":\"What advanced strategies are discussed for controlling output quality?\",\"answer\":\"The article addresses controlling parameters such as temperature and token limits, adapting prompts to specific domains, and managing ambiguous or incomplete user inputs.\"}]","Prompt Engineering for ChatGPT - A Quick Guide to Techniques, Tips, and Best Practices | PDF",43]