The Art of Chunking Data: Making Information Digestible for AI

Your AI is only as smart as the data you feed it. And how you break that data into pieces — how you chunk it — determines whether your AI understands context or gets hopelessly confused.

Chunking isn’t just splitting text at random intervals. It’s an art form that balances size, meaning, and retrievability.

Why Chunking Matters

Let’s start with the fundamental problem.

You have documents. Lots of them. Research papers, customer transcripts, technical manuals, legal contracts, company wikis. You want an AI to understand them, answer questions about them, generate insights from them.

But you can’t feed an entire encyclopedia to a language model every time someone asks a question. Token limits exist. Processing costs money. Retrieval needs precision.

So you chunk. You break documents into smaller pieces that can be embedded, stored in vector databases, and retrieved when relevant.

The question is: how do you chunk intelligently?

The Two Major Approaches

Chunking strategies fall into two categories, each with different strengths.