I’ve seen a lot when I’m working with enterprise AI teams: they nearly always blame the model when something goes wrong. This is understandable, but it’s also frequently incorrect, and it ends up being quite costly. The usual scenario is as follows. The outputs are inconsistent; when someone raises it, the first reaction is to blame the model. It may require more training data, another fine-tuning run, or a different base model. After weeks of work, the issue remains the same or has only slightly changed. The real problem, often sitting in the retrieval layer, the context window or how tasks were being routed, was never examined. I’ve seen it happen so many times before that I believe it is worth writing about. Fine-tuning is useful, but it gets overused In many cases, it’s still worthwhile to make a few adjustments. If domain adaptation, tone alignment, or safety calibration are required, it should be part of the workflow. I’m not saying that you shouldn’t use it.…