doubt-driven-development

Adversarial code review is a structured process that spawns a fresh-context reviewer to actively identify flaws, edge cases, and hidden coupling in high-risk architectural changes before finalization.

To find hidden assumptions in caching logic, apply an adversarial review process that forces rigorous questioning of non-trivial decisions through a structured cycle of claim presentation and systematic reconciliation.

Use an adversarial review process for high-stakes engineering decisions like security-sensitive code, high-risk production changes, and architectural modifications where correctness is more critical than development speed.

Systematically classify adversarial code review findings using a structured reconciliation framework that categorizes identified flaws and edge cases into actionable fixes, acceptable trade-offs, or disregarded noise.

Yes, adversarial review prevents the accumulation of unverified assumptions in long-running AI sessions by forcing rigorous, fresh-context questioning of non-trivial decisions before they are finalized.

The primary limitation of adversarial review is its overhead; it is designed for high-risk logic and architectural changes where correctness is paramount, making it less suitable for rapid development where speed outweighs strict verification.