For smaller datasets, annotation can feel straightforward. But once you push past a few tens of thousands of samples, a bottleneck seems to form almost out of nowhere — quality drops, inconsistency spikes, and QA starts lagging.
A few common pain points I keep running into:
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annotation drift between reviewers
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edge cases that weren’t covered in initial guidelines
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QC that worked early but collapses under volume
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scaling human reviews without losing quality
I was thinking about where these bottlenecks come from and what steps actually help break through them. This write-up I read framed a lot of the pain points and mitigation steps in a way that made sense to me:
https://aipersonic.com/blog/breaking-the-annotation-bottleneck/
Not sharing the link to promote anything — just to give some context for the discussion.
So for folks who’ve dealt with this at scale:
What actually helped you lessen the bottleneck?
Was it:
• better reviewer training?
• multi-stage QA?
• automation + human hybrid?
• clearer annotation guidelines?
• tooling that forces consistency?
Would love to hear real workflows that worked in practice.