How we benchmark over-refusal with research presets
Lawful fiction edge-cases, with/without preset, false-refusal rate as the metric. Method + checklist.
Short notes you can cite: how we measure over-refusal and test guardrails. Lawful research only.
Lawful fiction edge-cases, with/without preset, false-refusal rate as the metric. Method + checklist.
A decision rule for developers: API for harnesses and refusal benchmarks, chat for personas. Workflow + code.
Preset vs system prompt vs fine-tune: per-request scope, what relaxes on lawful creative, what stays refused.
Your app in the middle, RawLine as adversarial generator, lawful edge-cases in a loop. Metrics + harden cycle.
GEO + SEO template: definition first, method, raw rates, FAQ schema, llms.txt entry. Copy the frame.
For educational and research purposes only. Lawful use only.