Raw HTML bloats Retrieval-Augmented Generation (RAG) pipelines. An average web page consists of 80% markup and 20% actual content. Passing this raw Document Object Model (DOM) to a Large Language Model wastes tokens, increases latency, and severely degrades response quality. If you chunk and embed raw HTML, your vector database index becomes polluted with CSS class names, SVG paths, and tracking scripts. A similarity search for specific domain knowledge might incorrectly return a chunk containing layout classes instead of the actual textual content. The solution is moving the extraction and transformation logic to the edge. By converting raw web pages into clean Markdown or structured JSON at the scraping layer, you preserve semantic structure while eliminating token waste. The Token Economy of Web Data LLMs process text using tokenizers based on algorithms like Byte Pair Encoding (BPE).…