doubt-driven-development

Adversarial review isolates artifacts to subject architectural decisions to a disprove-first scrutiny cycle, catching hidden assumptions before irreversible production deployments by preventing cognitive bias accumulation in long-running AI sessions.

Adversarial code review is a disprove-first process that isolates artifacts from session history to subject security-sensitive logic to fresh-context scrutiny, identifying hidden failure modes through a multi-step doubt cycle of artifact extraction and findings reconciliation.

Perform an adversarial review during high-stakes operations like architectural changes, security-sensitive logic implementation, and irreversible production deployments to prevent silent assumptions from accumulating in long-running AI sessions.

Cross-model verification works by offering optional multi-model validation to catch blind spots inherent in single-model reasoning, operating on isolated artifacts and contracts rather than accumulated session history to ensure objective code review scrutiny.

The best way to review complex code changes without session bias is using context isolation, ensuring reviewers operate on isolated artifacts and contracts rather than accumulated session history to subject architectural decisions to adversarial, disprove-first review.

Avoid using a disprove-first review process for trivial code changes or low-stakes operations, as the overhead of artifact extraction, adversarial prompting, and reconciliation is designed specifically for non-trivial, high-stakes decisions and irreversible production deployments.