Authors : Dr. Payal Gulati

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/4vmw5hhj

Scribd : https://tinyurl.com/mrxb3ud3

DOI : https://doi.org/10.38124/ijisrt/25dec1606

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Modern machine learning systems rely on large volumes of high-quality labeled data. However, collecting real- world data for constructing complex datasets often expensive, time-consuming, and restricted due to privacy and ethical concerns. Machine-generated data has emerged as a reliable alternative to address these challenges. With the advancement of Large Language Models (LLMs), it has become possible to generate realistic, domain-specific, and task- oriented data at scale. It also leverages LLMs to create quality data without the need to manually collect, clean, and annotate huge or big datasets. This paper presents a detailed study of machine-generated data creation using LLMs. Building upon existing practical frameworks, the study proposes a structured pipeline that integrates prompt engineering, retrieval-augmented generation, quality filtering, and iterative refinement. The paper also discusses evaluation strategies, real-world applications, and ethical challenges, making the proposed approach suitable for both academic research and industrial deployment.

Keywords : Large Language Models, Data Augmentation, Retrieval-Augmented Generation, Privacy-Preserving Artificial Intelligence, and Machine Learning.

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