AI systems are notoriously under-documented. A model deployed without documentation becomes a black box that only its original developer understands, and even they forget the details after a few months. Good documentation is not bureaucratic overhead; it is the difference between a system that can be maintained, audited, and improved versus one that must be replaced when its creator leaves.

What to Document

System overview. What does the system do? What problem does it solve? Who uses it? What decisions does it inform or automate? Write this for someone who has never seen the system. This is the entry point for auditors, new team members, and stakeholders.

Architecture. How the system is built: data flows, model architecture, serving infrastructure, integration points, and dependencies. Include diagrams. Show how data moves from source through processing, training, inference, and output.

Data documentation. Training data sources, collection methods, preprocessing steps, known biases and limitations, data freshness, and update frequency. For each data source: who owns it, what license applies, what PII it contains, and how long it is retained.

Model documentation (model card). Following the model card framework: model details (type, version, framework), intended use, out-of-scope uses, training procedure, evaluation results (overall and per-slice), ethical considerations, and limitations. Model cards should accompany every model version in your registry.

Evaluation methodology. How the model was evaluated: metrics used, evaluation datasets, baseline comparisons, fairness analysis, and robustness testing results. This enables others to reproduce your evaluation and understand the basis for deployment decisions.

Operational documentation. How to deploy, monitor, and troubleshoot the system. Runbooks for common failure modes. Escalation procedures. On-call responsibilities. Monitoring dashboard locations and alert descriptions.

Decision log. Key design decisions and their rationale: why this model architecture, why this training approach, why these features, why this threshold. Decisions that seem obvious today become mysterious in six months.

Documentation Structure

Organize documentation in layers:

Quick reference - One-page summary with system purpose, current status, key contacts, and links to detailed documentation. This is what someone reads first.

Technical specification - Detailed architecture, data, and model documentation. This is the authoritative reference.

Operational runbook - Procedures for deployment, monitoring, incident response, and maintenance. This is what the on-call engineer reads at 2 AM.

Compliance package - Risk assessment, model cards, data impact assessments, and audit trail. This is what regulators and governance reviewers need.

Keeping Documentation Current

Documentation that is not maintained becomes actively misleading, which is worse than no documentation at all.

Automate what you can. Generate model cards from experiment tracking metadata. Extract architecture diagrams from infrastructure-as-code. Pull evaluation results directly from your evaluation pipeline. Automated documentation stays current because it is produced by the same systems that produce the models.

Tie documentation to deployment gates. Require updated documentation as a prerequisite for model deployment. If the model registry rejects a model version without a current model card, documentation stays current.

Schedule reviews. For high-risk systems, schedule quarterly documentation reviews. Compare documented behavior against actual behavior. Update decision logs when new decisions are made.

Assign ownership. Every document has an owner responsible for its accuracy. Ownership follows the system: the team that operates the model owns its documentation.

Documentation for the EU AI Act

The EU AI Act requires technical documentation for high-risk AI systems that includes: a general description of the system, detailed descriptions of development elements, monitoring and functioning information, and a description of the risk management system. Start documenting these elements now rather than scrambling when enforcement begins. The documentation requirements are detailed but align well with engineering best practices for maintainability.