22. LLM Ops: Monitoring Retrieval, Generation, and User Experience Signals

Description

An LLM application rarely crashes—instead, it degrades slowly. In production, your AI might look healthy on the outside, but underneath, retrieval could be getting weaker, and answers might be losing their grounding. In this video, we dive into the world of LLM Monitoring and explain why a "200 OK" status code isn't enough to ensure your system is still trustworthy. We break down the 3 critical layers of monitoring for real-world RAG systems: 1. Retrieval Signals: How to monitor Top-K results and similarity scores to catch root causes before the model ever starts generating. 2. Generation Signals: Tracking token usage, cost, and output validity. We discuss why truncation isn't just a cosmetic issue—it's a production failure. 3. Experience Signals: Beyond system health. We look at end-to-end latency, internal fallbacks, and the power of real-world user feedback (thumbs up/down). Monitoring in LLM Ops is your first line of defense. Learn how to catch hallucinations and quality drops before your users report them, and maintain the trust that is essential for any AI-powered product.