I’ve found a workflow that feels both structured and respectful of professional craft, especially in the context of this thread. I don’t just "vibe code" and let an LLM fill in the blanks. I use a classic design discipline (UML and use-cases) to document the process: 1. Start with requirements – 2.Define use cases - 3. Implement classes/objects (Architecture first, not after-the-fact refactors) 4. Add constraints and invariants (Contracts, boundaries, failure modes, etc.) - 5. Let the agent work inside that frame, pausing at milestones for human oversight.

Those UML/use-case/constraint artifacts aren’t committed as session logs per se, but they are part of the author’s intent and reasoning that gets committed alongside the resulting code. That gives future reviewers the why as well as the what, which is far more useful than a raw AI session transcript.

Stepping back, this feels like a decent and dignified position for a programmer in 2026: humans retain architectural judgement --> AI accelerates boilerplate and edge implementation --> version history still reflects intent and accountability rather than chat transcripts. I can’t afford to let go of the productivity gains that flow from using AI as part of a disciplined engineering process, but I also don’t think commit logs should become a dumping ground for unfiltered conversation history.