doubt-driven-development

Adversarial review prevents hidden errors in high-stakes production code by subjecting non-trivial decisions to a fresh-context analysis. This structured 5-step doubt cycle extracts claims, doubts them without validation bias, and reconciles actionable fixes before deployment.

Fresh-context adversarial review in software development spawns an unbiased reviewer focused on finding flaws rather than validating existing assumptions. It catches edge cases, unstated constraints, and hidden coupling in non-trivial code decisions.

Cross-model review can catch blind spots in architectural decisions by optionally requesting a second opinion from a different AI model. This identifies shared blind spots between the original author and single-model reviewer, requiring explicit user authorization for every invocation.

To run an adversarial code review without infinite cycles, use a bounded 5-step doubt cycle with a 3-cycle limit and clear stop conditions. This classifies reviewer findings to prioritize actionable fixes over noise, ensuring the review process terminates efficiently.

Adversarial review for unfamiliar codebases should be used when correctness outweighs speed, especially in security-sensitive logic, architectural decisions, or irreversible operations. It prevents accumulated context from turning unstated assumptions into perceived facts.

Adversarial review works for security-sensitive logic and irreversible operations by applying a structured doubt cycle that extracts claims and doubts them without validation bias. This enforces finding hidden errors and unstated constraints before deployment.