Thanks to the development of basic models and the high quality of the data, the emergence of AI-generated content has
accelerated. Despite its incredible success, there are still challenges that are yet to be addressed in AI content generation, such as
the processing of long-trail information, maintaining up-to-date knowledge, addressing high inference and training expenses,
and addressing data leakage. The shift to address those challenges has been called Retrieval Augmented Generation (RAG).
RAG has brought the process of gathering information, which improves the process of data generation by recovering relevant
information from available data sources, resulting in robustness and accuracy. RAG has become the foundation of Natural
Language Processing (NLP) to effectively fill the gap between factual accuracy of knowledge and fluency of “Large Language
Models (LLMs)”. This study traces the root of RAG from its beginnings as a framework for knowledge-based tasks to its current
state as an agentic, modular and complex runtime of knowledge. This study is an in-depth analysis of the evolution of Naïve
RAG to Modular and Advanced RAG models, and the introduction of new innovations, such as self-reflection, dense vector
recovery, and the use of different models. They are then examined to provide detailed feedback on how to make RAG truly
dynamic and usable as a verifier when applying them to organizations.
Mallikarjunarao Sunke, S. Gudi, Sriharsha Gudi· International Journal for Re...· 0 citations
There are concerns over cloud computing infrastructure as whether it could be sustainable for exponential growth of
AI workloads. In this era, the present research examines the trends on energy consumption on cloud data centers and proposes
that the use of energy efficient modules can help reduce the operational energy usage by approximately 70% without
compromising the quality of service. This study suggests a holistic approach to green computing in the cloud for AI applications,
which includes smart resource management, optimization of hardware and the use of modern cooling technologies. Based on
industry facts, empirical evidence and key cloud provider reports, this study examines the priority issues in the current
infrastructure and suggests solutions to these issues on the basis of empirical evidence. It is uncovered in the findings that more
than 15% of energy is used by AI data centers and they are projected to consume up to 50% by 2030. It is clear there is a high
level of consumption and fast action is required. This study evaluates the benefits of dynamic allocation of resources,
virtualization, specialized AI accelerators, energy-aware scheduling and adoption of green energy. The study results indicate that
the results of using several techniques in combination along with integrated techniques yield excellent results compared to
interferences separately. The performance of a TPU based system is better than a general-purpose GPU, and AI-based cooling
technology can reduce electricity consumption by approximately 40%. In addition, from on-premise to optimized cloud structure,
migration can minimize carbon emission by 80%. This research contributes valuable, concrete and theoretical strategies for
sustainable application of AI in cloud data centers, fulfilling a growing demand for computing.
S. Gudi· International Journal for Re...· 0 citations
Healthcare prescription automation is increasingly dependent on interoperable clinical data exchange, cloud-native service orchestration, secure application programming interfaces, and dependable software delivery pipelines. However, current prescription automation platforms often treat clinical decision support, e-prescribing workflow execution, software reliability engineering, cybersecurity governance, and deployment optimization as separate concerns. This separation creates architectural fragmentation in environments where prescription requests must be clinically valid, auditable, resilient to distributed failure, compliant with privacy obligations, and scalable under fluctuating enterprise workloads. This paper proposes a conceptual AI-driven cloud-native microservices framework for secure healthcare prescription automation, software reliability, and scalable deployment optimization. The framework integrates modular prescription services, AI-assisted clinical and operational intelligence, policy-driven security controls, observability-centered reliability engineering, and container-based deployment automation into a unified architecture. The major contribution of this paper is not an empirical claim of clinical superiority, but a structured reference model for designing prescription automation platforms that can support high-assurance workflows in regulated healthcare environments. The study identifies practical gaps in existing approaches, including a weak linkage between prescription standards and runtime reliability, insufficient integration of defect prediction with development pipelines, limited explainability in AI-assisted prescription decisions, and fragmented governance across APIs, containers, and machine learning components. The paper further presents a comparative methodology, architectural layers, implementation considerations, expected analytical outcomes, risk limitations, and future research directions. The proposed framework can guide healthcare enterprises, cloud architects, software reliability engineers, and AI governance teams in designing secure, scalable, and auditable prescription automation systems without binding the architecture to a single vendor or proprietary platform.
Srikanth Reddy Gudi· International Journal of Eme...· 0 citations
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