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Tag: AI Observability
Monitoring, tracing, and debugging techniques for AI systems.
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Why your AI architecture looks right on paper but fails in production
The whiteboard looks perfect. The pager does not. You can diagram a clean RAG pipeline in five minutes. Vector DB, LLM, a couple of services, job queue, done. It demoed…
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Versioning in LLM Systems: What Actually Matters in Production
The quiet failure that burns teams Most LLM incidents I get called into are not caused by GPUs catching fire or models forgetting how to English. They come from teams…
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Stop chasing model accuracy. Design for reliability.
The outage did not care about your 82% accuracy Your eval showed 82% accuracy last week. PagerDuty still went off at 2:13 AM because: The vector DB had a 99th…
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Why your AI evaluation metrics are misleading (and how to fix them)
The dashboard says 92% accuracy. Your users disagree. If your eval sheet shows high scores but support tickets are spiking, you do not have a model problem. You have a…
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Where Your AI Budget Quietly Leaks (and How to Plug It)
The quiet bleed Most AI invoices don’t explode. They bleed. A few extra tokens here, a lazy top_k there, a GPU pool idling at 6 percent because someone hard-coded min…
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