The Reality of Prompt Engineering: Simplicity Often Outperforms Sophistication in Reasoning Tasks

The Reality of Prompt Engineering: Simplicity Often Outperforms Sophistication in Reasoning Tasks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Reality of Prompt Engineering: Simplicity Often Outperforms Sophistication in Reasoning Tasks Abdulhamid Onawole, Anandhi Vivek Dhukaram, Adetola Adeniyi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7410360/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Despite the proliferation of prompt engineering techniques for large language models, research outputs present contradictory findings about optimal strategies, with some studies showing substantial improvements while others demonstrate minimal effects. We conducted a two-phase comprehensive evaluation to systematically assess prompt effectiveness across challenging reasoning tasks. Our primary investigation tested five prominent prompt engineering techniques (zero-shot, few-shot, chain-of-thought, role-playing, and deliberation) across 27 reasoning tasks from BIG-Bench Hard using GPT-4o-mini—chosen as a cost-efficient and accessible model for practical deployment scenarios. We selected reasoning tasks because they enable direct comparison to human cognitive performance and provide objective evaluation metrics. The initial phase tested 13,500 queries (2,700 questions across 5 prompt types). Building on these findings, our second phase introduced task-specific variants—specialized role-play prompts and task-specific few-shot examples—testing an additional 5,400 queries across the same tasks. Overall performance rankings revealed that task-specific role-play achieved the highest accuracy (87.78%), fol-lowed closely by chain-of-thought (87.63%) and role-playing (87.15%). Surprisingly, zero-shot prompting (84.85%) significantly outperformed few-shot prompting (80.70%) by 4.15 percentage points. A critical discovery emerged from response analysis: all prompt types spontaneously exhibit chain-of-thought-style reasoning patterns, even when not explicitly instructed, suggesting that step-by-step reasoning represents an internalized model behavior rather than a prompt-dependent phenomenon. Statistical analysis across both phases (18,900 total queries) revealed 14 significant performance differences (p < 0.05), with few-shot approaches consistently under-performing. Notably, role-play and zero-shot prompting proved most efficient in token usage and response time while maintaining competitive accuracy. The results demonstrate that prompt complexity does not guarantee superior performance, and that task-domain alignment matters more than assuming sophisticated prompts yield better results. Our findings provide evidence-based guidance challenging conventional assumptions about prompt engineering effectiveness. Artificial Intelligence and Machine Learning Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7410360","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502675696,"identity":"44fd05da-5a47-4b8d-a233-6a247221de56","order_by":0,"name":"Abdulhamid Onawole","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBADGQb2BiBlYEFYKQ+c5jkA0iJBihaJBBBNhBZ79sPHHvxss+Hhl3x+dcOPAgkG/vbuBPy28KSlG/a2pfFIzs4pu9kDdJjEmbMbCDgsx0yCd9thHoPbOWk3eIBaDCRyCWjhf2Mm+Reoxf7mmbSbf4jSIpFjJg22RYL92G3ibLnxLE1a9l8aj8SZHLbbMgYSPAT9wt6ffEzyzRkbOf72489uvvkDYvTi14JsoQGYJFY52MIHpKgeBaNgFIyCEQQASARA1J2dFGEAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Abdulhamid","middleName":"","lastName":"Onawole","suffix":""},{"id":502675697,"identity":"1e71fdc6-ac55-4503-9b8a-e1d659f20b5b","order_by":1,"name":"Anandhi Vivek Dhukaram","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Anandhi","middleName":"Vivek","lastName":"Dhukaram","suffix":""},{"id":502675698,"identity":"23c52d34-5f26-4a1e-bd89-cc3957d1045a","order_by":2,"name":"Adetola Adeniyi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Adetola","middleName":"","lastName":"Adeniyi","suffix":""}],"badges":[],"createdAt":"2025-08-19 16:02:51","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7410360/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7410360/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89526966,"identity":"7da09a92-7c60-4312-ad1e-ba91ed7352cd","added_by":"auto","created_at":"2025-08-21 02:36:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11166425,"visible":true,"origin":"","legend":"","description":"","filename":"realityofpromptengineering.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7410360/v1_covered_0ae7c72e-774a-46f8-b2d1-33494b648ec6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eThe Reality of Prompt Engineering: Simplicity Often Outperforms Sophistication in Reasoning Tasks\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7410360/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7410360/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite the proliferation of prompt engineering techniques for large language models, research outputs present contradictory findings about optimal strategies, with some studies showing substantial improvements while others demonstrate minimal effects. We conducted a two-phase comprehensive evaluation to systematically assess prompt effectiveness across challenging reasoning tasks. Our primary investigation tested five prominent prompt engineering techniques (zero-shot, few-shot, chain-of-thought, role-playing, and deliberation) across 27 reasoning tasks from BIG-Bench Hard using GPT-4o-mini—chosen as a cost-efficient and accessible model for practical deployment scenarios. We selected reasoning tasks because they enable direct comparison to human cognitive performance and provide objective evaluation metrics. The initial phase tested 13,500 queries (2,700 questions across 5 prompt types). Building on these findings, our second phase introduced task-specific variants—specialized role-play prompts and task-specific few-shot examples—testing an additional 5,400 queries across the same tasks. Overall performance rankings revealed that task-specific role-play achieved the highest accuracy (87.78%), fol-lowed closely by chain-of-thought (87.63%) and role-playing (87.15%). Surprisingly, zero-shot prompting (84.85%) significantly outperformed few-shot prompting (80.70%) by 4.15 percentage points. A critical discovery emerged from response analysis: all prompt types spontaneously exhibit chain-of-thought-style reasoning patterns, even when not explicitly instructed, suggesting that step-by-step reasoning represents an internalized model behavior rather than a prompt-dependent phenomenon. Statistical analysis across both phases (18,900 total queries) revealed 14 significant performance differences (p \u0026lt; 0.05), with few-shot approaches consistently under-performing. Notably, role-play and zero-shot prompting proved most efficient in token usage and response time while maintaining competitive accuracy. The results demonstrate that prompt complexity does not guarantee superior performance, and that task-domain alignment matters more than assuming sophisticated prompts yield better results. 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