We receive 500+ submissions daily. Manual review takes 6 hours/day. Testing Latenode with Claude to auto-approve/reject posts. It works for obvious spam but struggles with brand guideline nuances.
How do you train the AI to understand ‘on-brand’ content without constant manual tweaks?
Feed your approved content library into Claude as training data. Set up Latenode’s feedback loop – human overrides automatically improve the model. Our accuracy improved from 72% to 94% in 3 weeks.
Create brand alignment scores using historical data. We used Latenode to analyze 10k approved/rejected posts – now the system predicts acceptance probability with 89% accuracy.
Built a two-stage filter: 1) Claude checks policy compliance 2) GPT-4 evaluates brand alignment. Only 10% need human review now. Latenode’s routing nodes handle the workflow – costs 1/3 of human moderation.
Implement continuous learning – every moderator action trains the AI. Use Latenode’s version control to track model improvements. Set up A/B tests between human and AI decisions to identify knowledge gaps.
upload ur brand guidelines pdf to latenode. claude can refrence it live durring moderation
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