How do you decide which annotation cases an LLM can label versus which need a human?
LLM-assisted labeling works well on cases that follow predictable, well-established patterns — the model has seen enough similar examples to be reliably correct. Cases involving subjective interpretation, ambiguous context, or genuinely novel patterns still need a human. The practical mechanism is tracking agreement scores: when automated or multiple human labels disagree, that item escalates for expert review rather than being trusted blindly.
Answered in
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?
- Why use a tiered human review structure instead of one flat pool of annotators?
- 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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