AI IN CLOUD-BASED INFORMATION RETRIEVAL AN EXPLORATION OF CURRENT METHODS AND EMERGING CHALLENGES

Abstract

The fast merging of cloud computing and artificial intelligence has essentially changed the information retrieval (IR) systems in the modern era, facilitating access of vast, intelligent and context-sensitive data. The distributed storage, parallel processing and elastic resource provisioning, which is required to handle large and heterogeneous datasets, is availed by cloud platforms, and the accuracy and efficiency of retrieval is substantially increased through algorithms in AI, including machine learning, deep learning, semantic embeddings, and neural ranking models. The following paper gives an in-depth account of cloud-based IR architecture, including the service models, distributed data storage, indexing approaches, and query processing mechanisms. It looks at the ways AI techniques and distributed learning systems can be used to maximize search at scale, and how Large Language Models (LLMs) can be applied to semantic search, query re-formulation, and retrieval-augmented generation. Also, the paper points out some of the most important ethical and technical issues, such as algorithmic bias, model drift, and privacy that influence the equitable and trustable execution of AI-based IR systems. Altogether, this paper highlights the potential of AI-assisted cloud IR as the revolution but provides the importance of responsible, secure, and scalable retrieval models in the changing data landscape.