Guardrails ✦ 84outlineNot a production recipe — calibrate thresholds

LLM output policy check

Who: Trust & safety. Steps: State=model output + policies → Nouls → rewrite/block. Expected effect: Catch policy breaks before user sees.

Source: llm_guardrails ↗ Docs / source ↗

Treat this as an outline — adapt state and questions to your data.

Implement in code — call System One / Jev; compose answers yourself.

Gate on confidence — act, confirm, or escalate before side effects.

Outline sketch

Prompt

# LLM output policy check
State: relevant software state for this pattern.
Ask: Choice / Score / Noul questions that close the decision space.
Then: compose the route or action in code; escalate when confidence is low.

Needs access to: typesafe-sdk

Who it's for

Trust & safety

Steps / how it's set up

Steps

State=model output + policies.
Nouls.
rewrite/block.

Sources

Metrics and demo claims are author-reported or cookbook-reported unless you measure them yourself. Calibrate on your data.

Expected effect

Catch policy breaks before user sees

Unofficial outline for learning. Paraphrased from public docs and tutorials — not a production recipe. Review sources before you automate anything.