interrogate

Multi-model code reviews catch blind spots by spawning four reviewers across distinct models using the same prompt and rubric, synthesizing a final verdict from diverse perspectives to surface hidden risks without auto-applying changes.

Adversarial code review challenges assumptions by leveraging model diversity rather than assigned personas. It is needed for high-stakes reviews, security-sensitive patches, and complex design critiques where uncovering edge cases and hidden risks is critical.

To review code changes without trusting a single LLM, spawn four reviewers on four different models using a consistent rubric, then synthesize their independent perspectives into a final verdict that informs your decision-making.

Multi-model review automation does not auto-apply code changes. It outputs a synthesized verdict from independent model perspectives to inform your decision-making, ensuring human oversight over high-stakes or security-sensitive patches.

Adversarial review applies to complex design critiques as well as code changes. The approach uses diverse reasoning across four distinct models to uncover edge cases and hidden risks wherever diverse perspectives are valuable.

To orchestrate four-model adversarial reviews effectively, you need a consistent rubric, orchestration across four distinct models, and a synthesis step to generate a final verdict from the independent model perspectives.