What Is Chunking in LLMs? Understanding the Foundation of AI Document Processing

You're building an AI system to analyze customer contracts. You upload a 200-page agreement to ChatGPT and expect a comprehensive analysis. Instead, you get partial responses or errors. The problem? You've hit the chunk barrier—one of the most fundamental constraints in AI implementation that most executives don't understand.

The Direct Answer:

• Chunking is systematic text segmentation: Breaking large documents into smaller, digestible pieces (typically 300-500 words) that AI models can actually process within their memory limits​

• It's a technical necessity, not a choice: Every LLM has a fixed context window—a hard limit on how much text it can "see" at once—ranging from 4K to 128K tokens (roughly 3,000 to 96,000 words)​

• Think puzzle pieces, not pages: Each chunk must maintain enough context to be meaningful while staying small enough for the AI to process efficiently​.

Three Takeaways You Can Implement Today:

• Start with the 500-word rule of thumb. When feeding documents into AI systems, break content into chunks of roughly 500 words (about 650 tokens) with 10-15% overlap between sections. This preserves context at boundaries while respecting most models' processing limits. Your legal contracts? Chunk by clause groupings. Research reports? Split by methodology sections. This isn't arbitrary—it's architecting for how AI actually works.​

• Match chunking strategy to document type. As I've discussed at First AI Movers, effective AI implementation requires understanding the practical constraints available right now. Fixed-size chunks work brilliantly for structured documents like invoices or forms where layout matters more than narrative flow. But for strategy documents or customer feedback? Use semantic chunking that splits at natural topic boundaries, preserving meaning even if chunks vary in length.​

• Test your chunking before full deployment. Your focus should not be on hypothetical perfect chunking but on mastering the practical approach that works for your specific documents. Run small batches with different chunk sizes—300, 500, and 800 words—then compare retrieval accuracy and response quality. What works for financial reports may fail spectacularly for technical manuals.​

Real-World Example

Here's what I've learned from hands-on experiments with client implementations. An organization needed to process thousands of business records for analysis. Initial attempts using no chunking crashed their system—context windows maxed out immediately. We implemented semantic chunking by creating chunks that averaged 450 words with 50-word overlap. The result? Processing time dropped 75%, accuracy improved because the AI maintained episode context, and the system scaled to handle 10x document volume.​

Limits & Fixes

Context loss at chunk boundaries remains the primary technical challenge—the AI can't see across the artificial divisions you've created. The fix? Implement overlap zones where the last paragraph of one chunk repeats as the first paragraph of the next, ensuring continuity. Cost multiplication is equally real—more chunks mean more API calls. Counter this by using cheaper models for initial extraction phases and premium models only for complex reasoning stages.​

Stop treating AI like magic and start treating it like engineering. Take your most time-consuming document type, test three chunking strategies this week, and measure what actually works. Let's do this—together.

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