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APPLICATION OF GENERATIVE AI TO AUTOMATE ANALYTICAL REPORTS: OPPORTUNITIES, RISKS, AND QUALITY CONTROL METHODS

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dc.contributor.authorBauyrzhan Beisenbayev
dc.date.accessioned2025-12-29T09:32:35Z
dc.date.issued2025-12-27
dc.description.abstractThe article discusses modern approaches to the use of generative models (LLM ) for automating the preparation of analytical reports: architectures (including retrieval-augmented Generation ), practical benefits and economic effects, key risks (hallucinations , distortions , data leakage, compliance), as well as quality control and verification methods (technical and organizational). Practical recommendations for implementing generative AI in business analytics are offered.
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dc.identifier.urihttps://americanjournal.org/index.php/ajper/article/view/3293
dc.identifier.urihttps://asianeducationindex.com/handle/123456789/16129
dc.language.isoeng
dc.publisherAmerican Journals
dc.relationhttps://americanjournal.org/index.php/ajper/article/view/3293/3142
dc.rightshttps://creativecommons.org/licenses/by-nc/4.0
dc.sourceAmerican Journal of Pedagogical and Educational Research; Vol. 43 (2025); 131-139
dc.source2832-9791
dc.subjectgenerative artificial intelligence, automation of analytical reports, RAG, quality control, fact-checking, LLM hallucinations, prompt engineering, credibility assessment, corporate analytics, risk management
dc.titleAPPLICATION OF GENERATIVE AI TO AUTOMATE ANALYTICAL REPORTS: OPPORTUNITIES, RISKS, AND QUALITY CONTROL METHODS
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typePeer-reviewed Article

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