Application of AI in Solving Real-World Problems: A Retrieval-Augmented Generation Framework for Intelligent Information Access

This paper presents a Retrieval-Augmented
Generation (RAG) system that integrates AI and advanced
data retrieval techniques to address real-world challenges. The
system processes diverse data formats, including PDFs, web
links, videos, and images, leveraging Large Language Models
(LLMs) and vector databases for semantic understanding. Our
approach bridges the gap between traditional keyword-based
systems and modern AI-driven solutions, offering precise,
contextually relevant, and actionable insights. The paper
details the methodology, results, and potential applications
across domains like education, healthcare, and customer
service, highlighting its transformative potential in
simplifying information retrieval.