An Intelligent Nabl Laboratory Information System Using Retrieval-augmented Generation For Compliance Automation

Abstract

Laboratories Accredited By The National Accreditation Board For Testing And Calibration Laboratories (NABL) Handle Large Amounts Of Documentation Relating To Quality Manuals, Audit Trail Records, Standard Operating Procedures, And Compliance Documents. Traditional Systems For Managing Documents, Using Keywords For Search And Manual Navigation, Have Created Inefficiencies During Audits Or When Verifying Compliance.[4][5] As A Result, The Development Of AI Technologies, Including Retrieval-Augmented Generation (RAG), Indicates The Potential For Intelligent Ways To Retrieve Documents And Obtain Contextually Relevant Answers To Questions. In This Paper, We Will Outline Our Vision For A Laboratory Information System (LIS) Fueled By Artificial Intelligence (AI) Through The Integration Of RAG With The Current NABL Accreditation Workflow. Our Proposed LIS System Combines Semantic Document Retrieval From Vector Databases With Fact-based And Context-sensitive Responses Generated By Large-language Models. [1][3]. To This End, We Emphasize The Creation Of A Layered Architecture Based On Four Components: Document Preprocessing; Vector Embedding; Similarity Searches; And Natural-Language Generation (NLG) Components. Our Approach Significantly Enhances The Way Laboratories Audit Their Operations, Reduces The Time Required To Search For Documents, And Increases The Ability To Provide Traceable Responses Generated By The AI Component. Our Methodology Provides Laboratories With The Opportunity To Integrate RAG Technology Into Their Operations While Building A Framework For Building Trusted AI-based Accreditation Systems.[6],[10]

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