A Review of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) Systems, and Convolutional Neural Networks (CNNs) in Application Systems | Chapter 8 | Science and Technology: Developments and Applications Vol. 5

The rapid development of artificial intelligence (AI) has brought disruptive changes across many industries, with large language models (LLMs), Retrieval-Augmented Generation (RAG) systems, and convolutional neural networks (CNNs) featured prominently in this massive transformation. The advent of Artificial Intelligence (AI) in recent years has transformed the technology landscape like never before. More implementations of AI-powered applications have led to advanced and sophisticated supporting technologies such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, and Convolutional Neural Networks (CNNs). Application systems using these advanced technologies are transforming businesses across industries. LLM enhances natural language understanding and also aids in generation, facilitating communications closely resembling human interactions. RAG systems integrate retrieval mechanisms with generative capabilities, improving the relevance and accuracy of generated content by leveraging external knowledge bases. Meanwhile, CNNs continue to excel in image processing tasks, driving advancements in computer vision applications. The collective synergies between these technologies help businesses in improving efficiency, user experience, and decision-making.

The study illustrates implementations while revealing the challenges and opportunities presented by these technologies. The research on these technologies underscores the necessity for ongoing research and adaptation in leveraging these technologies to maximize their potential in real-world applications.

The study concluded that the technologies of LLMs, RAG, and CNNs have a far-reaching and disruptive effect on application systems, impacting many industries. LLMs are reshaping the landscape of natural language processing, allowing for more nuanced systems that comprehend, produce, and communicate human language.

 

Author (s) Details

 

Balagopal Ramdurai
IEEE, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/stda/v5/4045