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Tag: LLMOps
Operational practices for managing LLM-based applications in production.
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AI Observability: Stop Guessing, Start Instrumenting
The uncomfortable truth: you are flying blind Most AI incidents are not outages. They are quiet quality regressions, silent cost blowups, and vendor drift that no one notices for weeks….
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How to Build Real Feedback Loops Into AI Systems
The quiet failure of AI systems without feedback Most teams ship an LLM feature, celebrate a bump in usage, then stall. Quality plateaus, costs creep up, complaints trickle in, and…
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The true cost of self‑hosting LLMs vs using APIs
The real bill usually arrives at p95 I keep seeing the same pattern: a team proves out a feature on an API, gets a scary bill, then someone says “we…
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Scaling GenAI from PoC to Production: What Breaks and How to Fix It
The uncomfortable gap between a great demo and a stable product The PoC nails a few curated prompts. The team celebrates. Two weeks later the first production users show up…
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MLOps for LLMs: What Actually Matters in Production
The ugly part of LLMs: the system works until it silently doesn’t If your first LLM feature went live and then support tickets tripled, latency wandered, and your cloud bill…
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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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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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