Build AI systems that show their work (and learn over time)
TL;DR: This educational guide demonstrates how to build a lead-scoring AI system using no-code automation tools. The article walks through creating a traceable AI that scores leads as hot/warm/cold, explains its reasoning, allows manual review for high-stakes decisions, and implements feedback loops to improve over time. The approach emphasizes traceability (understanding why AI made each decision), human oversight for important decisions, and iterative improvement based on actual outcomes. Key components include Google Sheets for data storage, Make.com for workflow automation, OpenAI for AI scoring, and manual or automated feedback loops to track whether predictions matched reality. The guide advocates starting with simple manual tracking before automating, and using prompt versioning to understand what drives accuracy improvements.
Key Insights
- Traceable AI systems that explain decisions reduce risk and enable iterative improvement
- Start with manual feedback tracking before automating the learning loop
- No-code tools (Make.com + Google Sheets + OpenAI) can build production AI systems
- Version your prompts to separate accuracy gains from lead mix changes
Actionable Takeaways
- Require AI to output both decision and reasoning in structured format for traceability
- Add manual review gates for high-stakes decisions using Slack buttons and webhooks
- Track actual outcomes in a Feedback column to compare predictions against reality
- Version prompts when updating logic to measure true accuracy improvements
- Use Google Forms for fast manual feedback collection before building full automation