Asked about Splitting documents well
What is the trade-off in chunk size?
An answer that works
Small chunks retrieve precisely but often lack the context to be useful on their own. Large chunks carry context but dilute the embedding so they match many things weakly. The right size depends on the corpus and should be measured with a retrieval eval.
Then they push further
How would you choose the size for a particular set of documents?
Try a few sizes and measure retrieval on real questions with known source passages. The best size depends on how the documents are written and what people ask, so it is found by measurement, not by a rule.
What can you do besides picking one size?
Split on structure such as headings, attach the heading path or a short document summary to each chunk before embedding, or retrieve small chunks and pass the larger surrounding section to the model.
What gets marked up
Marked up for measuring against real questions and for structure-aware chunking. A single magic number with no reasoning is marked down.
Reading an answer is not being able to give one. The section that teaches this is Splitting documents well, 4 hours of Chunking & document processing.
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