Data Embedding and Retrieval in Generative AI: A Visual Exploration of Vector Databases and Prompting Techniques

Author(s)	Dr. Mayuri Manoj Bapat, Mr. Pankaj Mehta, Mr. Rushikesh Kashid
Country	India
Abstract	This paper explores the process of data embedding in Generative Artificial Intelligence (Gen-AI) models, focusing on how embeddings are efficiently stored and retrieved using various vector databases. We demonstrate how embeddings compress high-dimensional data while preserving semantic relationships, and visualize these embeddings to provide insights into their structure. Through practical examples, we highlight the use of vector databases, such as Chroma DB, in managing and querying embedded data. Additionally, we examine how prompting techniques improve the relevance and accuracy of AI-driven responses. The study discusses current challenges in data storage and retrieval, as well as potential avenues for future research to optimize vector-based systems.
Keywords	Data Embedding, Generative AI, Vector Database, Embedding Visualization, Chroma DB, Prompting Techniques, Data storage, Data retrieval
Field	Computer Applications
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-08-10
DOI	https://doi.org/10.36948/ijfmr.2026.v08i04.85062

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