How to Monitor LLMs in Production: 3 Steps to Stop Silent Failures

Описание: Is your AI silently failing in production? LLMOps monitoring is the framework that catches drift, cost spikes, and broken outputs before they become a $300K problem.

In this video, we break down what happens after you deploy a large language model into the real world and why "vibe-based engineering" — shipping because the demo felt good — leads to expensive blind spots. We walk through a simple 3-step framework anyone can follow: tracking cost and latency spikes, measuring output drift against a known-good baseline, and catching silent failures through user feedback logging.

You will see two practical examples: a customer service bot that starts replying in the wrong language without anyone noticing, and a bank's loan-document summarizer that quietly drops critical risk factors. Both show why monitoring is not optional once AI touches real users and real money.

We also cover the limits of monitoring — it only catches what you tell it to watch — and why aligning your dashboard with actual business goals matters more than tracking every possible metric.

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This video is for educational purposes only and does not constitute professional, financial, or legal advice.
Chapters:
0:00 Intro: What happens after you launch AI?
0:15 The agenda: problem, framework, ROI
0:25 The struggle: vibe-based engineering
0:48 The cost of inaction: $300K/year leak
1:15 The solution: LLMOps as a dashboard for AI
1:32 In practice: customer service bot gone wrong
1:52 The 3 steps: spikes, drift, silent failures
2:42 Deep dive: bank loan summary example
3:09 Risks and limits of monitoring
3:23 Closing: audit, track, measure

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