How I Built a File-Timestamp-Based Feedback Loop to Enforce AI Output Quality

Short summary

LLMs produce probabilistic outputs that fail edge cases regardless of prompt quality. The author built a deterministic feedback loop combining file-timestamp quality gates, Python script automation, and periodic AI-driven content regeneration. Key insight: let machines check (timestamps, exit codes), humans/AI judge (synthesis); the methodology scales from personal dogfooding through open-source contributions and back into production systems.

• •Closed-loop feedback uses file timestamps + Python scripts for deterministic quality gates
• •Separate mechanical checks (hard blocks on delivery) from soft reminders (low execution rates)
• •Open-source flywheel: dogfood → extract → gap-fill PR → merge back → production use

Generated with AI, which can make mistakes.