[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84058-en":3,"doc-seo-84058-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84058,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Prompt Coach: An Empirical Evaluation of an Agentic Tutor for Learning Prompt Engineering in Software Development","Prompt engineering is a crucial but undertaught skill for software developers, challenging to learn with conventional resources due to its evolving, interactive, and context-dependent nature. The paper introduces Prompt Coach (PC), an agentic IDE-embedded tutor that provides Socratic, in-flow guidance. PC evaluates prompt quality across multiple dimensions using the developer’s codebase and the target LLM’s behavior, prompting self-correction. An early study with 15 professionals shows significant post-session gains, strongest in often-overlooked dimensions.","Prompt Coach: An Empirical Evaluation of an Agentic Tutor for Learning Prompt Engineering in Software Development  \nRohit Mehra†, Kapil Singi†, Vikrant Kaulgud†, Vibhu Saujanya Sharma†, Swapnajeet Gon  \nChoudhury†, Swati Sharma‡, Adam P. Burden*, Majd Sakr*  \n†Accenture Labs, India ‡Accenture, India *Accenture, USA  \n{rohit.a.mehra,kapil.singi,vikrant.kaulgud,vibhu.sharma, [s.g.choudhury}@accenture.com](s.g.choudhury}@accenture.com)[ ](s.g.choudhury}@accenture.com){swati.c.sharma,adam.p.burden,[majd.sakr}@accenture.com](majd.sakr}@accenture.com)  \narXiv :2607 .06074v 1 [ cs . SE] 7 Jul 2026  \nAbstract  \nPrompt engineering has emerged as a critical yet undertaught skill for software developers, one that traditional learning approaches are ill-equipped to support given its evolving, interactive, and context-dependent nature. In this paper, we introduce Prompt Coach (PC), an agentic tutor that helps developers learn how to craft high-quality code-generation prompts through Socratic guidance embedded in-flow within their IDE. PC evaluates prompt quality across multiple dimensions and surfaces targeted questions to guide self-correction, grounded in the developer’s codebase and the behavior of the target LLM. We present an early empirical study with 15 professional developers combining quantitative prompt quality scoring with qualitative perception measures. Participants showed statistically significant improvements after a single 60-minute session, with the largest gains across dimensions commonly overlooked by developers. They also reported strong trust, high adoption readiness, and unanimous agreement that PC improved their prompt-writing skills.  \n1 Introduction  \nWith the rapid proliferation of generative and agentic AI, software engineering skills are undergoing a fundamental transformation [2] . Many long-standing skills are becoming obsolete, while a new class of skills is emerging and evolving faster than developers can adapt [14, 16] . Recent industry reports project that 80% of developers will need to upgrade their skills by 2027 to remain market relevant [13] . The focus is shifting from mastering static tools, languages, and frameworks to sustained cognitive collaboration with generative and agentic AI systems. Developers are now required not only to produce correct and efficient code, but also to explicitly articulate intent, constraints, context, and quality expectations through natural language interactions that shape AI-generated artifacts [35] . Prompt engineering has consequently emerged as a first-class engineering skill, with organizations increasingly seeking dedicated expertise in this area [12, 36]. Yet, acquiring such skills efficiently and effectively has become an urgent need, one that traditional learning approaches are ill-equipped to meet.  \nWe posit that, despite their widespread adoption and proven effectiveness in teaching stable programming concepts, traditional learning models (such as video tutorials, books, documentation, online courses, etc.) have fundamental limitations that make them illsuited for emerging human-AI collaboration skills such as prompt engineering. These limitations include:  \n• Lack of context: Prompt engineering is deeply tied to the developer’s task, codebase, and workflow, whereas videosand tutorials remain generic and detached from real development contexts [11] .  \n• One-size-fits-all calibration: Developers vary widely in their baseline skills and experience, yet traditional content is typically designed for a single, assumed learner and lacks the ability to adapt dynamically to individual needs [23] .  \n• Inherently interactive practice: Prompt engineering skill develops through iterative cycles of prompting, observing AI-generated code, and refining prompts, a process that static formats cannot effectively support.  \n• Rapid content obsolescence: Static learning materials quickly become outdated as AI systems evolve, often failing to reflect current model behaviors and","cbCaisbYt752I71S","https://ap.wps.com/l/cbCaisbYt752I71S","pdf",1652674,1,7,"English","en",105,"# Abstract\n# Introduction\n## Limitations of traditional learning approaches\n## Rationale for agentic tutors","[{\"question\":\"What problem does Prompt Coach address in software development learning?\",\"answer\":\"Prompt Coach targets the difficulty of acquiring prompt engineering skills, which are evolving, interactive, and dependent on developer context and the target LLM’s behavior.\"},{\"question\":\"How does Prompt Coach deliver learning support to developers?\",\"answer\":\"It acts as an agentic tutor embedded in the IDE, using Socratic guidance and targeted questions. It evaluates prompt quality across multiple dimensions to drive self-correction grounded in the developer’s codebase.\"},{\"question\":\"What evidence does the paper provide for Prompt Coach’s effectiveness?\",\"answer\":\"The authors report an early empirical study with 15 professional developers. 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