Module 5: Document Processing & Chunking Slides

Slide Outline

1. Document Processing & Chunking - Chunking strategies, data cleaning, metadata enrichment, and building ingestion pipelines
2. Learning Objectives - 4 outcomes for this module
3. Why This Module Matters - The ingestion pipeline determines what your RAG system can find. Bad chunking means bad retrieval means bad answers. Mos
4. Chunking Strategies - Lesson section from the full module
5. Metadata Enrichment - Lesson section from the full module
6. Handling Different Document Types - Lesson section from the full module
7. Common Mistakes to Avoid - 4 mistakes covered
8. Hands-On Labs - 2 hands-on labs
9. Key Takeaways - 5 points to remember

Learning Objectives

• Design chunking strategies for different document types
• Build robust document ingestion pipelines
• Implement metadata enrichment for better retrieval
• Handle PDFs, HTML, Markdown, and structured data

Why This Module Matters

The ingestion pipeline determines what your RAG system can find. Bad chunking means bad retrieval means bad answers. Most production RAG failures trace back to poor document processing, not poor models.

Common Mistakes

• Using one chunk size for all document types
• Not adding metadata to chunks (cannot filter by source, date, category)
• Chunking code the same way as prose (functions should be complete units)
• Not handling PDF tables and images (silently lost content)

Key Takeaways

• Chunk size is the most impactful RAG parameter — too small loses context, too large dilutes relevance
• Sweet spot: 200-500 tokens with 10-20% overlap
• Metadata enrichment enables filtering — critical for multi-tenant and large corpora
• Different document types need different parsing strategies
• Build ingestion as a pipeline: parse -> clean -> chunk -> enrich -> embed -> store

Hands-On Labs

1. Build a Document Ingestion Pipeline

   Process multiple document types into chunked, embedded vectors.

   35 min - Intermediate

   • Parse PDFs, Markdown, and HTML documents
   • Implement fixed-size and semantic chunking
   • Enrich chunks with metadata (title, source, section)
   • Embed and store in Qdrant

   View lab files on GitHub

2. Compare Chunking Strategies

   Measure the impact of chunk size on retrieval quality.

   30 min - Intermediate

   • Chunk the same corpus with 3 different strategies
   • Run identical queries against each
   • Measure precision and recall at different chunk sizes
   • Document the optimal strategy for your data

   View lab files on GitHub