Applies the SIFT media-literacy framework — Stop, Investigate the source, Find better coverage, Trace claims — to evaluate the credibility and reliability of every engine’s response.
How it works
SIFT is a four-step lateral reading discipline designed to cut through misinformation. When enabled, the judge engine applies each step to the full set of AI responses:
Stop — flags impulsive acceptance of any single engine’s answer
Investigate the source — evaluates the provenance and credibility of cited facts
Find better coverage — surfaces whether other engines or sources corroborate the claims
Trace claims — follows assertions back to their original context to check for distortion
Produces a reliability verdict with confidence levels per engine
SIFT is particularly effective for news, health, and political topics where AI models frequently repeat contested claims without attribution.
When to use SIFT Method
Verifying health or medical information before acting on it
Evaluating breaking news where facts are still evolving
Checking AI-generated citations for accuracy and source quality
Detecting misinformation patterns across multiple model outputs
Media literacy education and critical thinking research