🔗 The art of document chunking for LLM applications

When building document-aware AI applications, one of the most consequential yet under-appreciated technical decisions is how to divide documents into smaller pieces or "chunks." This process, known as chunking, can dramatically impact retrieval quality, context relevance, and ultimately the performance of your AI system.

Why Chunking Matters

Large language models (LLMs) have context length limitations. Even with models that support tens of thousands of tokens, we often need to select the most relevant portions of documents to include in a prompt. Effective chunking helps ensure that:

1. Retrieved information is coherent: Each chunk contains complete thoughts or ideas
2. Relevant content stays together: Related information isn't split across different chunks
3. Context isn't lost: Important surrounding context is preserved
4. Retrieval is precise: Chunks are specific enough to target exact information needs