PRODUCTION SCENARIO
A Generative AI Engineer chunks lab protocols at 1,600 tokens and embeds them with a model whose maximum input is 512 tokens. Questions about steps near the end of each protocol return nothing relevant, while questions about the opening steps work.
What should the engineer change in the pipeline?
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Show answer and explanation
Answer: Re-chunk the protocols at 400 tokens and embed them again at that size
Chunks longer than the embedding model's maximum token limit are truncated, so text past 512 tokens never enters a vector and cannot be retrieved at any depth, which is why only the ends of protocols are unreachable. Chunking at 400 tokens puts every chunk inside the limit so the whole protocol gets embedded. Embedding dimension sets vector width, not how much input the model reads. Overlap still submits 1,600-token chunks. Truncating each protocol to 512 tokens discards the later steps outright.