Optimizing Information Retrieval in RAG through Intelligent Reranking and Follow-Up Query Predictions

Optimizing Information Retrieval in RAG through Intelligent Reranking and Follow-Up Query Predictions | 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 Optimizing Information Retrieval in RAG through Intelligent Reranking and Follow-Up Query Predictions Kok Rhui Ong, Dr Wai Peng Wong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7506627/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 Reranking is a crucial process in Retrieval-Augmented Generation (RAG) systems as it significantly impacts the quality and the relevance of retrieved knowledge chunks. Conventional reranking models usually prioritize semantic similarity and matching accuracy between user queries and knowledge base embeddings. This causes them to often lack the ability to dynamically adapt to the evolving context of user interactions. In this paper, we propose a novel reranking framework designed to fill this gap and enhance retrieval in RAG systems by incorporating LLM-generated predicted follow-up queries coupled with the initial user query to better capture the evolving user intent. Our model leverages a fine-tuned weighting mechanism to balance the embeddings of the initial query and predicted follow-up queries, enabling context-aware reranking of knowledge chunks. The proposed approach tackles critical challenges, including enhancing personalization, scalability and ensuring relevance in scenarios where user queries are dynamic and context dependent. It addresses the need for adaptive retrieval mechanisms that can effectively handle evolving user intent and context to improve the quality of retrieved information. Evaluation on two benchmark datasets demonstrates that our reranking framework improves retrieval quality, effectively integrating user intent prediction to optimize the RAG process. Our results highlight the potential of embedding-driven, adaptive reranking models to advance the capabilities of RAG systems and pave the way for more intelligent information retrieval applications. Reranking Information Retrieval Retrieval Augmented Generation (RAG) Large Language Models (LLMs) Natural Language Processing (NLP) 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-7506627","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508419920,"identity":"4d65d53e-6410-4337-92e9-dd0d99e691f4","order_by":0,"name":"Kok Rhui Ong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBACCQY2BmYQBQSMD4AEDx8pWpgNQFrYiNQCBmxgjQS1SLa3pX0ubLNg4Jduv1b5NcdOBmjEw0c38GiR5jl2ePbMNgkGyTlnym7LbksGOozN2DgHjxY5ifRmZl6gFoMbOWm3JbcxA7XwsEkTraVYcls9YS3SEmmHoVrSjzF+3HaYsBbJnmPJzDznJHgkZ+QwSzNuO87DxkzALxLH24yZecrq5Pgl0h9+/Lmt2p6fvfnhY3xaYIAHiAyYeUBMZkJqEYD9AeMP4lWPglEwCkbBCAIA64Q63OfqDXMAAAAASUVORK5CYII=","orcid":"","institution":"Monash University Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Kok","middleName":"Rhui","lastName":"Ong","suffix":""},{"id":508419921,"identity":"aac6ef57-f468-45f3-9f23-1a3ab734f8f1","order_by":1,"name":"Dr Wai Peng Wong","email":"","orcid":"","institution":"Monash University Malaysia","correspondingAuthor":false,"prefix":"Dr","firstName":"Wai","middleName":"Peng","lastName":"Wong","suffix":""}],"badges":[],"createdAt":"2025-09-01 09:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7506627/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7506627/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92811966,"identity":"ae62ec35-757a-44b2-9923-4d8443ef22bc","added_by":"auto","created_at":"2025-10-05 16:32:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":631614,"visible":true,"origin":"","legend":"","description":"","filename":"RAG.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7506627/v1_covered_c0a4943a-97a0-4c30-9755-0a27fae41c26.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimizing Information Retrieval in RAG through Intelligent Reranking and Follow-Up Query Predictions","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":"Reranking, Information Retrieval, Retrieval Augmented Generation (RAG), Large Language Models (LLMs), Natural Language Processing (NLP)","lastPublishedDoi":"10.21203/rs.3.rs-7506627/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7506627/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReranking is a crucial process in Retrieval-Augmented Generation (RAG) systems as it significantly impacts the quality and the relevance of retrieved knowledge chunks. 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