santa-method

Automate post-generation verification by running two independent reviewers to validate outputs before shipping. This dual-review approach enforces convergence on a strict rubric, ensuring accuracy and compliance for high-stakes code, documentation, and customer-facing material.

Prevent hallucinations in generated content by applying a structured dual-review rubric with context isolation. Using fresh reviewer instances per iteration stops context leakage, catching misalignment and errors before customer-facing material is shipped.

Dual independent review for AI quality assurance works by running two separate reviewers over generated output. It implements a deterministic convergence loop with a fixed iteration cap, escalating to handle failures if the reviewers cannot converge on a validated output.

You can use automated dual-review verification for both code and documentation. This approach is suitable for any high-stakes end-user outputs requiring high assurance, including product prompts, where accuracy, compliance, and brand integrity are critical.

When independent reviewers fail to converge during output verification, the system handles the failure through escalation. The deterministic convergence loop uses a fixed iteration cap to prevent infinite loops before triggering this escalation process.

Context isolation matters in output verification because it uses fresh reviewer instances per iteration to prevent context leakage. This ensures each independent review remains unbiased, reducing the risk of unnoticed errors in high-stakes content.