SubExtract for AI Developers

Convert webpages, documentation sites, and YouTube transcripts into clean LLM-ready Markdown for RAG pipelines, fine-tuning datasets, and AI workflow context.

Workflows

Build a RAG corpus from a docs site

  1. Use Web Crawler with the docs site's root URL as the starting point
  2. Set crawl depth and page limit appropriately
  3. Get the bundled Markdown export — one file per URL
  4. Ingest into your vector store (Pinecone, Weaviate, pgvector, etc.) with the URL as the source metadata
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Convert a YouTube tutorial to LLM context

  1. Extract the YouTube video's transcript (no timestamps for cleanest LLM input)
  2. Save as a .txt or .md file
  3. Drop into your LLM tool (ChatGPT, Claude Projects, Perplexity Spaces) as context
  4. Now you can ask questions about the video without rewatching
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Extract a single article for a one-shot LLM prompt

  1. Paste any article URL into the Web Scraper
  2. Get clean Markdown output — no nav, no ads, no boilerplate
  3. Copy the output and paste into your LLM as context
  4. Ideal for summarization, fact-checking, or comparative analysis prompts
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Recommended tool combinations

LLM context preparation

Quick context gathering for one-shot LLM prompts — paste, scrape, drop into your prompt.

Real-world examples

Indexing a product's docs into a chatbot

Crawl the product's documentation site (typically 50-200 pages). Each page becomes a chunk in your vector store with the URL as source metadata. Now your chatbot can answer customer questions citing the exact docs page — clean Markdown means clean retrieval.

Researching for a long-form prompt

Need to write a thorough analysis prompt for Claude or GPT-4? Scrape 5 source articles into Markdown, concatenate, and paste as context. Token-efficient (no HTML overhead) and grounds the model in real source material.

Frequently asked questions

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