Kapa.ai RAG context pruning classifier

Small LLM taught to prune 68% of RAG context; uses binary per-token classification to remove irrelevant passages

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.5.13

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://kapa.ai/blog/how-we-prune-rag-context

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 “Kapa.ai RAG context pruning classifier”?",
  "input": "<input to classify>",
  "output": "one label from a fixed list"
}

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