Methodology

Every RebaseNest article passes through a dual-LLM fact-check pipeline before publication: Claude Opus 4 performs the primary review and GPT-5 performs an adversarial second pass. The intent is that any numeric claim, statute reference, or date must hold up against an independent model with a different training distribution.

Prompt template

Reviewers run with a fixed instruction: “verify every numeric claim, statute reference, and date against the cited primary source; flag anything unverifiable.” Disagreement surfaces a diff for a human editor.

Review cadence

Every published post is re-run quarterly. When a fact changes (rate update, new circular, statute amendment), the article’s dateModified is bumped and the byline’s “Reviewed” date refreshes.

Human-in-the-loop

A RebaseNest team member resolves any disagreement between the two models before publish. AI-assisted drafting, human-edited, dual-model fact-checked.

Disclosure

RebaseNest content is decision-support material, not investment, tax, or legal advice. See canonical sources for the corpus we cite.