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Cover image for Converting documents for LLM processing — A modern approach
Simeon Emanuilov
Simeon Emanuilov

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Converting documents for LLM processing — A modern approach

Processing documents for LLM training or AI pipelines often means dealing with thousands of files in various formats.

After encountering this challenge repeatedly in my work, I developed Monkt - a tool that helps transform documents and URLs into structured formats like JSON or Markdown.

The common challenges

  • Maintaining format consistency across different document types
  • Preserving structural elements (headers, tables, relationships)
  • Scaling the conversion process efficiently

Best practices for document processing

  • Preserve semantic structure: Maintain document hierarchy, relationships between headers, sections, and lists.
  • Handle mixed content: Process both text and non-text elements consistently, including images and tables.
  • Implement quality validation: Use automated checks and schemas to catch structural errors.
  • Design for scale: Utilize batch operations, parallel processing, and caching mechanisms.

A modern approach

Rather than combining multiple Python libraries (pdf2text, docx, BeautifulSoup, markitdown), modern document processing should focus on:

  • Automated format handling
  • Consistent structure preservation
  • Flexible output formats (Markdown/JSON)
  • Efficient caching for improved performance

The quality of your document conversion directly impacts both model training efficiency and inference accuracy.

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