Prompt Engineering for Large Language Models: A Systematic Review and Future Directions

Prompt Engineering for Large Language Models: A Systematic Review and Future Directions | 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 Systematic Review Prompt Engineering for Large Language Models: A Systematic Review and Future Directions Jothi Prakash B S, Barath Kannan D, Pankaj Seervi A, Meivezhi G D This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6551106/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 The rapid evolution of large language models (LLMs) has significantly transformed various domains within artificial intelligence (AI) and natural language processing (NLP). Despite their widespread adoption, the discipline of prompt engineering, which is fundamental to maximizing the potential of LLMs, remains insufficiently explored. This systematic review aims to bridge this gap by critically analyzing existing methodologies, identifying prevailing challenges, and outlining prospective research directions. A thorough examination of literature indexed in ACM, IEEE Xplore, and SpringerLink, covering publications from 2018 to 2024, underscores the absence of standardized frameworks in prompt design, considerable variability in prompt effectiveness across diverse applications, and ethical concerns related to bias and model interpretability. To address these challenges, this study advocates for the development of adaptive prompt optimization techniques, reinforcement learning-driven prompt refinement, and the incorporation of explainable AI frameworks. The insights presented in this review provide a comprehensive perspective on the current state of prompt engineering and offer valuable recommendations to guide future advancements in AI and NLP research. Prompt Engineering Large Language Models (LLMs) Systematic Review Natural Language Processing (NLP) AI Model Optimization Ethical AI Full Text Additional Declarations No competing interests reported. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6551106","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":451917069,"identity":"4f694d59-c4d6-443f-afcb-221f46fdb6f5","order_by":0,"name":"Jothi Prakash B S","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIie2QsQrCMBRFXyk4iXNERD8h0sFF9FfyEOqie8eAEDe7SQU/wk94ENAl6Ae4FATngoubRuviknZ0yJluknvgEgCP5w8JZHh8CD76xPLiGxxKQ7A8icsy1VEAmv12bnSZCar7EKYGOKrzZLjRy3sBo+6eQpU7h2ULEqguuLugYgRxtKdgxd1KS5BVBOugssM0WkUxp5KagUR1mlhlWRA8ayhyHoEwFGQdlHYY1VCyOAaRTNEqihk+jba6Qhmk+hA8+NgOm12LJBl318fVza3In+P7q0JX39KrePd4PB4PwAt9OFINrV4q4gAAAABJRU5ErkJggg==","orcid":"","institution":"SRM Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Jothi","middleName":"Prakash B","lastName":"S","suffix":""},{"id":451917072,"identity":"c6a19dc6-a0c5-42d2-94b1-6f503e3566e1","order_by":1,"name":"Barath Kannan D","email":"","orcid":"","institution":"SRM Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Barath","middleName":"Kannan","lastName":"D","suffix":""},{"id":451917076,"identity":"d675ec3f-3fcc-4fc2-844a-90d3ef3618d6","order_by":2,"name":"Pankaj Seervi A","email":"","orcid":"","institution":"SRM Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Pankaj","middleName":"Seervi","lastName":"A","suffix":""},{"id":451917077,"identity":"5b334c2f-fac8-48c6-983a-3bb88c42811a","order_by":3,"name":"Meivezhi G D","email":"","orcid":"","institution":"SRM Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Meivezhi","middleName":"G","lastName":"D","suffix":""}],"badges":[],"createdAt":"2025-04-28 23:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6551106/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6551106/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83285070,"identity":"1a4b01ee-86bc-4b7c-ae47-47de373c57db","added_by":"auto","created_at":"2025-05-22 11:08:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1490835,"visible":true,"origin":"","legend":"","description":"","filename":"PromptEngineeringforLargeLanguageModels.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6551106/v1_covered_51dee9f2-9b85-42b9-8078-d1645588ee5c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prompt Engineering for Large Language Models: A Systematic Review and Future Directions","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":"Prompt Engineering, Large Language Models (LLMs), Systematic Review, Natural Language Processing (NLP), AI Model Optimization, Ethical AI","lastPublishedDoi":"10.21203/rs.3.rs-6551106/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6551106/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid evolution of large language models (LLMs) has significantly transformed various domains within artificial intelligence (AI) and natural language processing (NLP). Despite their widespread adoption, the discipline of prompt engineering, which is fundamental to maximizing the potential of LLMs, remains insufficiently explored. This systematic review aims to bridge this gap by critically analyzing existing methodologies, identifying prevailing challenges, and outlining prospective research directions. A thorough examination of literature indexed in ACM, IEEE Xplore, and SpringerLink, covering publications from 2018 to 2024, underscores the absence of standardized frameworks in prompt design, considerable variability in prompt effectiveness across diverse applications, and ethical concerns related to bias and model interpretability. To address these challenges, this study advocates for the development of adaptive prompt optimization techniques, reinforcement learning-driven prompt refinement, and the incorporation of explainable AI frameworks. The insights presented in this review provide a comprehensive perspective on the current state of prompt engineering and offer valuable recommendations to guide future advancements in AI and NLP research.\u003c/p\u003e","manuscriptTitle":"Prompt Engineering for Large Language Models: A Systematic Review and Future Directions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 03:19:17","doi":"10.21203/rs.3.rs-6551106/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"cc651bd0-4570-45af-a5b3-dd6f317557c0","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-22T11:08:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 03:19:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6551106","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6551106","identity":"rs-6551106","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}