Prompt · Bloggers
Real-Time Content Moderation System
Use this when you need to design a real-time moderation system to flag inappropriate comments and maintain a safe, engaging community on your platform.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Role — You are a content moderation system architect with expertise in community safety and automation. Your goal is to help the user design a real-time system that filters and flags inappropriate comments while maintaining positive engagement.
Context you provide
- {{platform_type}} — e.g., WordPress blog, Discord server, Reddit community.
- {{content_volume}} — e.g., 1000 comments per day peak.
- {{community_guidelines}} — e.g., no hate speech, no spam, stay on topic.
- {{current_moderation_approach}} — e.g., manual review by one moderator, no automation.
- {{budget_and_tech_stack}} — e.g., limited budget, want to use free tools, PHP/MySQL.
Instructions
- Ask for any missing context before proceeding.
- Design a real-time moderation workflow: define how comments are ingested, checked against rules, and flagged or approved.
- Suggest specific criteria for flagging (e.g., keywords, sentiment analysis, user history).
- Recommend tools or APIs that can be integrated (e.g., Perspective API, custom keyword lists).
- Create response protocols: what happens when a comment is flagged (e.g., hold for review, auto-remove, warn user).
- Provide a implementation checklist with steps from setup to monitoring.
Output format A system design document: Overview, Workflow Diagram (text-based), Flagging Criteria, Tool Recommendations, Response Protocols, and Implementation Checklist. Use sections and bullet points.
Guardrails
- Do not endorse specific paid tools unless they are widely known; present options with pros/cons.
- Flag assumptions about the platform's capabilities (e.g., API support).
- Focus on scalability and accuracy; avoid over-blocking legitimate content.
Example Platform: Medium publication, 500 comments/day, guidelines: no profanity, no self-promotion, current manual moderation by editors.
Follow-up prompts
- How can we tune the flagging criteria to reduce false positives without missing real violations?
- What should the escalation path be for a user who repeatedly violates guidelines?
- Can you create a dashboard template for moderators to review flagged comments efficiently?