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Turn a written content policy into a moderation and escalation flow
Extract typed attributes from policy and use deterministic rules for publish/reject/review rather than letting a model decide final action.
Goal
Produce auditable policy checks and a human-review queue for submitted content.
Steps
- Have the policy owner define explicit rules/thresholds and use synthetic ad text/images as test cases. [moderation]
- Extract typed content attributes and compile the policy into a readable, auditable rule table as shown in the notebook. [moderation]
- Use a deterministic rule engine to return publish/reject/review and matched rule IDs; escalate conflicts or uncovered cases. [moderation]
How to verify
Compare rule outcomes with policy-reviewer labels on a labeled set; each decision includes matched rule IDs.
Troubleshooting
Route missing attributes to review conservatively; version rules and retain decision evidence.
Permissions
Prerequisites
- Python 3.11+, Anthropic API key, written policy, and synthetic or authorized examples.
Permissions
- Redact content first; route ambiguous/high-risk content to humans and never auto-publish by default.
Limitations
The notebook uses synthetic data and illustrative rules; policy owners must approve real publish/reject actions, and this is not legal judgment.
Pinned upstream source
- Repository
- anthropics/anthropic-cookbook
- File path
- capabilities/content_moderation/guide.ipynb
- Pinned commit
9c1e493ddd2b82df399d2a61b9c806c539e18be6- Source checked
- 2026-10-14 · Reviewed against the pinned upstream source
Repository metrics
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License: MIT
Main topic
Make Agent behavior safer and easier to verify