Why Clean Markdown is Essential for LLM RAG Pipelines
Modern Large Language Models (LLMs) like GPT-4o, Claude 3.7 Sonnet, DeepSeek-R1, and Llama 3.3 are trained extensively on plain text and Markdown. When raw HTML is fed into a prompt, up to 80% of the context window is wasted on styling tags, nested wrappers, JavaScript snippets, tracking pixels, and navigation menus.
FlyCrawl's proprietary Fit-Markdown algorithm eliminates this noise before context ingestion, reducing prompt token costs by up to 90% while dramatically improving retrieval accuracy in RAG vector databases.
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