A Field Guide to AI Documentation: Model Cards, Eval Reports, Agent Cards, and More
Every developer learns the same documentation types. The README. The API reference. Code comments. Maybe an architecture doc if the team is disciplined. That was the whole vocabulary for decades. Then AI arrived and quietly created an entire new category of documentation — one nobody taught us to write, that isn't in any bootcamp or CS degree, and that you're increasingly expected to produce…
AI development has introduced a new category of documentation that wasn't part of traditional software development practices. These new documents, often called "model cards," "eval reports," and "agent cards," serve to provide the trust needed in AI systems, where code alone can no longer guarantee behavior consistency.
Model cards are concise structured documents that outline a single trained model's purpose, training data, evaluation results, limitations, and restricted uses. They are akin to nutrition labels for models, providing a quick overview of what's included and how it should be used. Model cards are a regulatory requirement under the EU AI Act for high-risk systems, and they are essential when sharing or handing off models to others.
Datasheets for datasets are the counterpart to model cards, focusing on the data side of AI systems. They provide information about the dataset's origins, collection methods, composition, known biases, consent and licensing details, and appropriate use cases. Just as datasheets are crucial for understanding model behavior, they help address the many incidents that arise from inadequate data provenance.
System cards take the concept further, encompassing the entire AI system rather than just one model. They describe the complete AI pipeline, including architecture, safety evaluations, operational constraints, and deployment context. System cards are vital when dealing with complex AI stacks, as they help determine whether the entire system meets specific requirements.
Evaluation reports, also known as eval factsheets, document the methodology behind AI system evaluations. They include test sets, methodologies, assumptions behind metrics, and confidence levels associated with the results. These reports are crucial for any claim about an AI system's performance, especially for customer-facing agents. By detailing how scores were obtained, eval reports help determine whether they can be trusted.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
