RAG Isn’t So Easy: Why LLM Apps are Challenging

Unstructured introduces a content-aware chunking strategy for Retrieval-Augmented Generation (RAG) systems, offering higher quality outputs compared to traditional character-based chunking. This approach identifies document elements like titles and body text to create coherent segments, leading to more relevant responses and precise citations in natural language applications. In a test using GPT-4, Unstructured chunking proved more effective in providing detailed and comprehensive responses, especially when content is dispersed across multiple sections or documents. This method enhances the retrieval process by focusing on semantically consistent chunks, improving both the relevance of query results and the number of citations in responses.