AI Documentation Explained: Model Cards, Eval Reports, and the New Compliance Landscape

As AI systems become embedded in software products, developers are increasingly expected to produce a new category of documentation that traditional training never covered. Unlike classic docs describing deterministic code behavior, AI documentation addresses four key questions: what a model was trained on, how it was evaluated, what it is permitted to do, and what it actually did. Core document types include model cards — structured summaries of a model's training data, intended uses, and limitations — and datasheets that describe the provenance, composition, and known biases of datasets. These formats, pioneered by researchers like Mitchell et al. and Gebru et al., are now standard on platforms like Hugging Face and are becoming legal requirements under frameworks such as the EU AI Act for high-risk systems. Rather than mere paperwork, these artifacts serve as trust mechanisms in a probabilistic world where code alone can no longer guarantee consistent, auditable behavior.