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What you'll learn

Parse PDFs, DOCX, and HTML into structured Markdown
Build normalization pipelines for consistent AI input
Implement chunking strategies optimized for RAG retrieval
Handle edge cases: tables, images, multi-column layouts

Overview

Document parsing pipelines (299K+ stars) are critical infrastructure for RAG systems, knowledge bases, and AI-powered search. This course teaches you to build robust pipelines that turn messy documents into clean, structured Markdown.

What you'll build

A multi-format parser handling PDF, DOCX, HTML, and images
A Markdown normalization pipeline for consistent AI input
A chunking strategy for optimal RAG retrieval

Tools covered

Docling — IBM's document parser
MarkItDown — Microsoft's HTML-to-Markdown converter
Unstructured — Open-source document processing
pdf-parse / mammoth — Lightweight JS parsers

Why this matters

Garbage in, garbage out. The quality of AI outputs depends directly on the quality of document ingestion. This is the unglamorous but essential skill behind every production RAG system.