Retrieval and Structuring Augmented Generation with LLMs for Web Applications

Although Large Language Models (LLMs) have revolutionized natural language processing, they face significant challenges in web applications, including hallucinations, outdated knowledge, and limited specialization in niche domains. To address these issues, this tutorial explores how integrating retrieval mechanisms and structured knowledge can enhance LLM performance for web use. By leveraging Retrieval-Augmented Generation (RAG), we can ground LLM outputs with relevant external data, mitigating limitations in applications like search engines, chatbots, and recommendation systems. We delve into text structuring techniques-such as taxonomy construction, multi-level text classification, and taxonomy-guided information retrieval-that improve the effectiveness of information retrieval processes. Furthermore, we examine how structure-guided augmented generation through information extraction and knowledge graph construction can reduce hallucinations and enhance factual accuracy. By bridging the gap between unstructured language models and structured knowledge, we aim to unlock new potentials for dynamic web content generation and personalized user experiences. Finally, we highlight future directions for seamlessly integrating retrieval and generation, enhancing personalization, and incorporating multimodal data to expand the capabilities of LLMs in web applications.