Most teams obsess over models. GPT-4 vs Claude. Embeddings A vs B. Context window size. Meanwhile, chunking is treated like a preprocessing step — something you “just do.”
That’s a mistake.
Chunking Decides What the Model Sees
Retrieval doesn’t retrieve documents. It retrieves chunks. If your chunks are poorly constructed, no model can compensate.
- Too small → you lose meaning.
- Too large → you lose precision.
- Poorly segmented → you retrieve noise.
Key Insight
No model can fix what bad chunking breaks. The quality ceiling of your RAG system is set by your chunking strategy.
The Hidden Cost of Bad Chunking
Bad chunking leads to a cascade of downstream failures:
- Irrelevant context polluting the prompt.
- Partial answers that miss key information.
- Overconfident hallucinations from fragmented context.
- High latency from bloated context windows.
And because chunking happens early, every downstream step inherits the damage.
One Size Fits None
There is no “best” chunking strategy. The right strategy depends on:
- Document structure and format.
- Query type and complexity.
- Retrieval method and ranking.
- Evaluation goals and metrics.
That’s why chunking should be experimented with, not assumed.
At Noesia, chunking isn’t a checkbox. It’s a first-class decision. Because understanding starts with structure.