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The Role of Data Modeling in the world of RAG Models and Generative AI

International Journal of Computer Techniques – Volume 11 Issue 6, November 2024 | ISSN 2394-2231

Kishore Gade, Stuart Green
Vice President, Lead Software Engineer at JPMorgan Chase, USA
Email: kishore.gade@jpmchase.com
Principal Data Modeler, New York City, New York Metropolitan Area, USA
Email: stuart.green@example.com

Abstract

AI has changed with the advents of Retrieval-Augmented Generations (RAG) models & Generative AI, highlighting how important data modelling is to creating high performing & contextually relevant systems by guaranteeing effective information retrieval, creation & integration, data modelling, the act of structuring & arranging data to correspond with specific objectives & forms the basis for these AI systems. Data modelling makes creating the organized knowledge bases in RAG models easier, allowing generating capabilities & the retrieval techniques to work together seamlessly. Curating and preprocessing datasets is essential for generative AI to improve the outputs’ quality, coherence, and accuracy. This study examines the relationship between data modelling, RAG models, and generative AI, emphasizing industry best practices, obstacles, and new developments. It highlights the needs for strong data to overcome the barriers like hallucinations & data biases by discussing how they affect essential elements like scalability, domain flexibility & user-centric applications. The article also explores how data modelling techniques have been changed to meet the growing complexity & variety of unstructured data that these AI systems use. This study demonstrates the significant influences of well-designed data models on promoting innovations & operational excellence via an analysis of case studies & real-life applications. Data modelling becomes more strategically essential to success as more & more enterprises use RAG & generative AI for applications ranging from decision support systems to customized content productions. This abstract provides insights into the synergies between the structured data designs & the state-of-the-art Artificial Intelligence technologies, highlighting the importance of data modelling in allowing RAG & generative Artificial Intelligence to realize their full potential.

Keywords

Data Modeling · Generative AI · Retrieval-Augmented Generation (RAG) · Machine Learning · Data Architecture · Artificial Intelligence · Knowledge Graphs · Natural Language Processing (NLP) · Large Language Models (LLMs) · Data Preprocessing · Vector Databases · Data Integration · Scalable AI Systems · Data Bias · Data Quality · Emerging AI Trends

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About the Authors

Kishore Gade
Vice President, Lead Software Engineer at JPMorgan Chase, USA
Email: kishore.gade@jpmchase.com

Stuart Green
Principal Data Modeler, New York City, New York Metropolitan Area, USA
Email: stuart.green@example.com

International Journal of Computer Techniques – Volume 11 Issue 6, November 2024 | ISSN 2394-2231

The Role of Data Modeling in the world of RAG Models and Generative AI-Download