Introduction

Model documentation is essential for governance but tedious and often neglected. Large language models can automatically generate model documentation (descriptions of inputs, outputs, logic, limitations) from code, training logs, and metadata, reducing documentation burden and ensuring completeness.

Automated Documentation Generation

Feed LLM: model code, training logs, feature lists, performance metrics, test results. Prompt: "Generate comprehensive model documentation." LLM produces markdown/PDF documentation including model purpose, inputs, outputs, training methodology, performance, known limitations, recommendations. Human reviews and edits for accuracy; incorporates feedback.

Benefits

Reduces documentation burden (hours → minutes). Ensures consistency across models. Catches missing documentation earlier. Enables quick onboarding of new team members. Facilitates regulatory audits (complete documentation on demand).

Conclusion

LLM-powered documentation automation reduces governance overhead while improving documentation completeness and quality.