Many enterprises have adopted Retrieval-Augmented Generation (RAG) systems expecting to easily access corporate knowledge by indexing PDFs and connecting these to large language models (LLMs). However, in industries like engineering, RAG systems frequently fall short because they fail to preserve the logical structure of complex technical documents. The common practice of “fixed-size chunking” chops documents into arbitrary sections of text, disrupting tables and separating related content, causing inaccuracies in answers retrieved by the system. The solution lies in semantic chunking, which segments documents based on their actual structure—sections, paragraphs, and entire tables—maintaining logical cohesion. Additionally, critical visual content like diagrams and flowcharts, often skipped in standard RAG implementations, can be included through multimodal textualization, where images are analyzed, described in natural language, and embedded alongside text data. Moreover, an evidence-based UI with visual citations enhances user trust by linking answers directly to their source visuals or tables. Looking ahead, native multimodal embeddings promise more integrated handling of text and images, while advances in long-context LLMs may reduce the need for chunking altogether. For RAG systems to be reliable in production, they must respect document structure and unlock the rich data hidden in visual elements, transforming from mere keyword search tools to true knowledge assistants.
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