Automated Documentation Generation
Description
Automated documentation generation creates and maintains up-to-date documentation using various methods including programmatic scripts, large language models (LLMs), and extraction tools. These approaches can capture model architectures, data schemas, feature importance, performance metrics, API specifications, and lineage information without manual writing. Methods range from traditional code parsing and template-based generation to modern AI-assisted documentation that can understand context and generate human-readable explanations.
Example Use Cases
Transparency
Automatically generating comprehensive model cards for a healthcare AI system each time a new version is deployed, including updated performance metrics across demographic groups, data lineage information, and bias evaluation results for regulatory compliance documentation.
Using LLM-powered tools to automatically document complex financial risk models by analysing code, extracting business logic, and generating human-readable explanations of model behaviour for audit trails and stakeholder communication.
Reliability
Implementing automated API documentation generation for a machine learning platform that extracts endpoint specifications, parameter definitions, and usage examples, ensuring documentation stays synchronised with code changes and reducing deployment errors from outdated documentation.
Limitations
- AI-generated documentation may miss critical domain context and business logic that human experts would include, potentially leading to incomplete or misleading explanations of model behaviour.
- Template-based approaches often struggle with unstructured information and complex relationships between code components, limiting their ability to capture nuanced system interactions.
- Quality heavily depends on code quality and instrumentation comprehensiveness; poorly commented or documented source code will result in inadequate generated documentation.
- Maintenance overhead can be significant as automated systems require configuration updates when code structures change, and generated content may need human review for accuracy and completeness.
- LLM-based approaches may introduce hallucinations or inaccuracies, particularly when documenting complex technical details or domain-specific terminology without proper validation mechanisms.