How to Convert Webpages into Clean Markdown for LLM and RAG Pipelines

Feeding raw HTML into large language models wastes token budget on markup, ads, and navigation elements that can confuse the model and dilute actual content. Converting pages to clean Markdown — preserving headings, paragraphs, and links while stripping boilerplate — is the recommended approach for RAG and summarization pipelines. Standard extraction libraries fail on JavaScript-heavy single-page apps built with React, Next.js, or Vue, since the initial HTML response is often an empty shell until client-side code runs. A practical workaround involves detecting SPA framework signatures early and adaptively fetching more content, avoiding the overhead of a full headless browser. Preserving link structure, heading hierarchy, and word count metadata in the output further improves downstream chunking and citation accuracy within RAG systems.