My Loss Went Down, But My Model Still Broke — So I Built a Drift Metric
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I spent the last year building quality gates for AI agent outputs — deterministic verification, diff reviews, delivery checks. I even shipped one for the ECC project (200K+ stars). It worked. Then I started fine-tuning models. Training loss dropped from 9.2 to 8.8. Solid convergence. Everything looked great. So I ran a test prompt: 19999999999999999999999999999999... Every prompt. Every time. Perplexity never flagged it. That's when I realized: this quality-gate philosophy applies at the weight…
1Key Takeaways
- I spent the last year building quality gates for AI agent outputs — deterministic verification, diff reviews, delivery checks.
- I even shipped one for the ECC project (200K+ stars).
- Training loss dropped from 9.2 to 8.8.
- So I ran a test prompt: 19999999999999999999999999999999...
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — AI reports that i spent the last year building quality gates for AI agent outputs — deterministic verification, diff reviews, delivery checks.
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