ai-project-director-command-governance

AI project directive workflows maintain consistency by applying evidence-driven governance rules for task generation, review, and closure. This prevents drift across long-running conversations by ensuring all actions are traceable to a master product baseline.

Assigning different AI models to specific software engineering tasks is managed through standardized model assignment rules. This approach selects models like DeepSeek or Codex based on task nature, enhancing both accuracy and safety.

Runtime evidence in AI-driven software engineering is verified by ensuring all evidence records and commit states are traceable to a master product baseline. This structured review process confirms adherence to the established product baseline.

Yes, a consistent product baseline document is required before starting AI project governance. The governance process depends on this baseline to verify runtime evidence, manage task closure, and prevent workflow drift.

Instruction workflow control governs task generation by defining how ChatGPT generates and reviews task instructions for AI Project Director closure work. This ensures structured work and prevents unreviewed instructions from drifting.

Yes, this governance framework prevents workflow drift in evidence-driven AI development by enforcing structured work rules, evidence verification, and closure processes. It ensures all tasks and reviews remain anchored to the product baseline.