Introduction We've all been there. You have a folder full of PDFs, a list of URLs, or hours of audio, and you need to turn them into structured data. Traditionally, this meant: Custom Python scripts with Beautiful Soup or Selenium. Brittle regex patterns for PDFs that break on the slightest layout change. Manual transcription for audio. It's slow, error-prone, and a maintenance nightmare. The Shift: AI-Native Extraction With the rise of Large Language Models (LLMs), the game has changed. Instead of telling the computer how to find data (e.g., "look for the text after 'Invoice Total'"), we can tell it what to find (e.g., "Find the total amount and the currency"). In this post, I'll share how I built Snapparse to handle this at scale, and the technical challenges I faced along the way. Technical Challenge 1: Context Window vs. File Size Handling a 50-page PDF or a 100MB audio file isn't as simple as dumping it into an API. You need: Chunking : Breaking down large documents without losing context.…