Query complexity binary classification for models
Train classifier on benchmark data to predict which model will succeed; route to weak or strong backend
AA clean typed decision (yes/no, a pick from a list, or a level on a scale) that is concrete, repeats routinely, and has meaningful value.4.95
Key facts
- Vertical
- Software & tech
- Function
- Agents & dev
- Status
- Seen in the wild
- Volume
- high
- Value
- meaningful
- Risk
- moderate
- Evidence
- described plan
- Flags
- None
Source: https://docs.nvidia.com/nemo/switchyard/routing/llm-classifier-routing.md
Build this with a classifier
Define a typed decision with a bounded answer, then evaluate it on examples.
{
"decision_type": "choice",
"question": "Does this input match the decision in “Query complexity binary classification for models”?",
"input": "<input to classify>",
"output": "one label from a fixed list"
}Related use cases
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