Clear description of the AI system's purpose, intended use cases, target users, and expected outcomes. Include scope, limitations, and key functionalities.

Technical architecture, system components, data flows, and integration with third-party models or services. Include version information and dependencies.

Data sources, collection methods, representativeness analysis, preprocessing steps, quality assurance, and bias testing procedures.

Training methodology, evaluation frameworks, performance metrics, validation results, and model performance across different scenarios and demographics.

Identified risks, risk assessment methodology, mitigation strategies, residual risks, and risk monitoring procedures.

Human oversight mechanisms, intervention procedures, fail-safe systems, override capabilities, and escalation protocols.

Performance targets, robustness testing, security measures, adversarial testing, and continuous monitoring protocols.

Logging systems, traceability mechanisms, audit trails, data retention policies, and monitoring capabilities.

User instructions, operational guidelines, system limitations, known issues, and best practices for safe deployment.

Monitoring strategy, incident detection, feedback collection, performance tracking, and continuous improvement processes.