Development of RAG-Based Generative AI Application: Application of Generative AI in RAG-Based Web Development

Abstract The rapid evolution of Generative Artificial Intelligence (Generative AI) and Large Language Models (LLMs) has transformed the landscape of intelligent web application development. Despite their remarkable fluency, standalone LLMs frequently suffer from hallucination, outdated knowledge, and an inability to reference verifiable, domain-specific information, which limits their reliability in enterprise contexts. Retrieval-Augmented Generation (RAG) addresses these limitations by coupling a parametric language model with a non-parametric retrieval mechanism that fetches relevant information from an external knowledge base at inference time. This paper presents the design, methodology, and prototype architecture of a RAG-based generative AI web application intended to demonstrate how retrieval, semantic search, and prompt engineering can be combined with modern web development practices to build context-aware, factually grounded AI systems. The proposed system integrates a document ingestion and chunking pipeline, sentence-level embedding generation, a vector database for high-dimensional semantic search, and a large language model layer responsible for grounded response generation. The web application is designed using a React.js/Next.js frontend, a FastAPI/Node.js backend, JWT-based authentication, and a cloud-native deployment strategy using Docker, Kubernetes, and CI/CD pipelines on platforms such as AWS, Azure, and Google Cloud Platform. The paper reviews recent literature on RAG architectures, compares leading vector databases (Pinecone, FAISS, ChromaDB, Milvus) and embedding models, and surveys contributions from OpenAI, Anthropic, Google DeepMind, Meta AI, Microsoft Research, Hugging Face, LangChain, and LlamaIndex. Rather than reporting fabricated experimental numbers, this work proposes a rigorous evaluation methodology — covering retrieval accuracy, groundedness, latency, and user satisfaction — that can be applied once the prototype is fully deployed. The study concludes that RAG-based architectures offer a practical, scalable, and enterprise-ready pathway toward building trustworthy generative AI web applications, while identifying open challenges such as embedding quality, context-window constraints, and operational cost that motivate future work in agentic and multimodal RAG systems. Keywords: Generative AI, Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Prompt Engineering, Vector Database, Semantic Search, AI Chatbot, Web Development, Cloud Deployment, Enterprise Applications.