Скачать или смотреть Smart Chunking Strategy | Extracting 200+ Pages of Apple Health Data

Watch how to overcome Claude's context window limits by strategically chunking large document extraction into manageable, organized outputs.

📄 THE CHALLENGE
200+ pages of Apple Health FHIR data spanning 12 years. Claude's context window, message size, and response limits make single-pass extraction impossible.

📦 THE SOLUTION: TWO-DIMENSIONAL CHUNKING
Organize extraction along two axes:
Axis 1: Clinical Domain (Lab Results, Vitals, Medications, Clinical Data)
Axis 2: Time Period (Year-by-year partitioning)

🎯 THE MULTI-ARTIFACT DISCOVERY
Claude's code artifacts aren't just for displaying code—they're a powerful mechanism for outputting large datasets:
1. Extract one year's data
2. Output as downloadable JSON artifact
3. Proceed to next year
4. Download complete, organized dataset

📊 RESULTS DEMONSTRATED
13 yearly JSON files (2013-2025)
1,100+ lab result entries
100% extraction accuracy verified
All Claude platform limits respected

🔬 WHY THIS WORKS
Instead of fighting platform constraints, work with them. Strategic chunking by domain AND time creates manageable extraction units while maintaining data organization and validation.

📚 RESOURCES
Blog Post:   / from-data-graveyard-to-living-intelligence...  
GitHub Repo: https://github.com/georgevetticaden/m...
Portfolio: https://agenticmindset.ai/agent-portf...

🏷️ TAGS
#Claude #AIAgents #DataExtraction #AppleHealth #ContextWindow #DocumentProcessing #JSONSchema #HealthTech #MultiAgentSystems #AgenticMindset