Why use a tiered human review structure instead of one flat pool of annotators?
Most annotation cases are routine and can be handled quickly by trained core annotators applying consistent standards. A minority are genuinely ambiguous or subjective and need deeper judgment. Routing the easy majority to a fast first-pass tier and escalating only the hard cases to expert reviewers keeps both throughput and quality high, instead of forcing every case through the same expensive, slow review path.
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Why Your ML Team's Real Bottleneck Is Annotation, Not Model ChoiceIsolated annotation tooling doesn't scale. A shared platform with tiered human review plus LLM-assisted labeling does — here's the architecture.
Read the full analysisOther questions this article answers
- Why does annotation become a bottleneck as an ML team grows?
- How do you decide which annotation cases an LLM can label versus which need a human?
- What's the actual measurable benefit of centralizing annotation into a shared platform?
- Why does interoperable, tool-agnostic infrastructure matter for an annotation platform?
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