Chunk Size Matters for Enterprise LLMs

This article focuses on a practical problem in enterprise LLM applications: how to choose embedding models and chunk sizes for retrieval-augmented systems.

The main lesson is that more dimensions in an embedding model do not automatically mean better results. The right model still has to be tested against the actual use case, and in Actalyst’s experiments, chunk size often mattered more than the choice of embedding model.

The article explains why chunking is necessary, why naive splitting can lose context, and why teams may need overlap, metadata, summaries, or multiple chunking strategies depending on the question being asked.

The takeaway is pragmatic: there is no universal chunk size. Teams need to test representative questions, compare failure modes, and choose the approach that is least wrong for the production workflow.