ai-monitoring
Know When Your AI Breaks in Production
Guide the user through monitoring AI quality, safety, and cost in production. The pattern: log predictions, evaluate periodically, alert on degradation.
When you need monitoring
- Any AI feature running in production
- After launching something built with the other skills
- After any model or prompt change
- When compliance requires ongoing evidence that AI works correctly
- When you can't afford to discover problems from customer complaints
What can go wrong (without monitoring)
| Problem | How it happens | Impact |
|---|---|---|
| Silent model changes | Provider updates model behavior | Accuracy drops, nobody notices for weeks |
| Input drift | Users start asking questions you didn't train for | Quality degrades on new use cases |
| Gradual degradation | Prompts rot as data distribution shifts | Slow decline — death by a thousand cuts |
More from lebsral/dspy-programming-not-prompting-lms-skills
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Measure and improve how well your AI works. Use when AI gives wrong answers, accuracy is bad, responses are unreliable, you need to test AI quality, evaluate your AI, write metrics, benchmark performance, optimize prompts, improve results, or systematically make your AI better. Also used for spent hours tweaking prompts, trial and error prompt engineering is not working, quality plateaued early, stale prompts everywhere in your codebase, my AI is only 60% accurate, how to measure AI quality, AI evaluation framework, benchmark my LLM, prompt optimization not working, systematic way to improve AI, AI accuracy plateaued, DSPy optimizer tutorial, MIPROv2 optimization, how to go from 70% to 90% accuracy.
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