# Introduction A model that says it is 90% confident should be right 90% of the time. When that relationship breaks down, you get a miscalibration problem. The model’s scores stop telling you anything useful about reliability. For large language models (LLMs), miscalibration is widespread. A 2024 NAACL survey found that confidence scores diverge […]
The 4-Stage AI Asset Lifecycle: How to Manage Your Models, Datasets, and Labels Without Losing Track
TL;DR Every machine learning project produces three core assets: labeled datasets, trained models, and the schemas that define how labels are structured. Most teams manage code with Git, infrastructure with Terraform, and models with… nothing systematic. The result is duplicated work, untraceable training data, models in production that nobody can reproduce, and compliance gaps that […]
A Deep Dive into Calibration of Language Models: Platt Scaling, Isotonic Regression, Temperature Scaling
# Introduction A model that says it is 90% confident should be right 90% of the time. When that relationship breaks down, you get a miscalibration problem. The model’s scores stop telling you anything useful about reliability. For large language models (LLMs), miscalibration is widespread. A 2024 NAACL survey found that confidence scores diverge […]