Modern Web Development Using Retrieval-Augmented Generation (RAG): A Comprehensive Review

Abstract Web development has evolved from static hypertext delivery into a dynamic, AI-assisted engineering discipline in which conversational and generative interfaces increasingly mediate user interaction. Large Language Models (LLMs) have accelerated this shift by enabling natural-language interfaces, automated code assistance and intelligent content retrieval within web applications, yet their static training corpora, bounded context windows and susceptibility to hallucination limit their reliability in knowledge-intensive, production settings. Retrieval-Augmented Generation (RAG) has emerged as a practical remedy, coupling a non-parametric retrieval mechanism with a generative language model so that responses are grounded in externally retrieved, up-to-date evidence rather than relying solely on parametric memory. This review synthesises the RAG paradigm as it applies to modern web development, covering the retrieval pipeline (embeddings, chunking, vector indexing, similarity search, prompt augmentation and response generation), widely used vector databases (Pinecone, FAISS, ChromaDB, Milvus, Weaviate), embedding models (OpenAI, BGE, Sentence-Transformers, E5, Cohere) and orchestration frameworks (LangChain, LlamaIndex, Haystack, LangGraph), together with their integration into contemporary frontend, backend, database, cloud and containerised deployment stacks. Literature published between 2023 and 2025 was reviewed to identify emerging directions, including hybrid search, agentic RAG, multimodal RAG, Graph RAG, adaptive RAG, Self-RAG and corrective RAG. Comparative analysis across traditional search, fine-tuning and RAG architectures indicates that RAG offers a favourable balance of factual grounding, cost efficiency and maintainability for web-scale deployments, although challenges of latency, retrieval quality and evaluation persist. The review concludes with open research directions for building reliable, retrieval-grounded web applications. Keywords: Retrieval-Augmented Generation, Large Language Models, Vector Databases, Semantic Search, Embeddings, Web Development, LangChain, Prompt Engineering.