The work that happens after the exciting part is over.
AI deployments aren’t “set it and forget it.” They’re closer to a new employee in their first 90 days — they need continuous feedback, course correction, and someone watching the output.
Key Concepts
- LLM Drift — The model changed. Your output quality changed with it.
- Silent Failure — When everything looks fine but nothing is working
- Knowledge Base Decay — The facts rot. The AI keeps answering confidently.
Monitoring Tools
- Langfuse — Token usage tracking, cost per request, output quality scoring, and LLM-as-judge evaluation on production traces
- Prompt Engineering — Version prompts like code; the golden eval set is your regression suite
Drift & Quality
- LLM Drift — The model changed. Your output quality changed with it. Monitor or find out from users.
- Silent Failure — When everything looks fine but nothing is working
- Hallucination Failure — Confident wrong answers that require detection, not just uptime monitoring
- Knowledge Base Decay — The facts rot. The AI keeps answering confidently.
Cost Control
- Cost Overrun — When runaway retry loops drain budgets overnight
- TCO — The maintenance tax: 20-30% of build cost annually; budget for it before launch
Operations is what keeps a deployment alive after the exciting part is over. Monitoring output quality, not just uptime, is what separates AI deployments that compound over time from ones that quietly fail.