Feeding raw HTML into a Retrieval-Augmented Generation (RAG) pipeline is computationally expensive and highly inefficient. Large Language Models (LLMs) operate on tokens, and HTML DOM structures are notoriously token-heavy. When you pipe raw HTML into an embedding model or an LLM context window, you are paying for structural noise: nested <div> tags, class names, SVG paths, and inline styles that offer zero semantic value to the language model. To optimize data ingestion for RAG applications, data engineers are shifting from raw HTML extraction to semantic Markdown extraction. Markdown preserves the hierarchical structure of a document—headers, lists, tables, and links—while stripping away the rendering boilerplate. This significantly reduces token consumption, lowers inference costs, and improves the retrieval accuracy of vector databases by increasing the signal-to-noise ratio in your document chunks. The Token Economics of HTML vs.…