According to God of Prompt on X, context stacking outperforms “act as an expert” prompts across 200+ tests on Claude, ChatGPT, and Gemini, because it feeds verifiable constraints and artifacts rather than role-play claims. As reported by the original X thread, the method layers: 1) objective, 2) deliverable format, 3) source constraints, 4) domain definitions, and 5) evaluation rubric, which reduced hallucinations and tightened adherence to business requirements. According to the X post, measurable gains included higher factual precision on tasks like policy drafting, technical summaries, and marketing copy when inputs included citations, glossaries, and acceptance criteria. As reported by the same source, teams can operationalize this by templating reusable blocks—purpose, audience, canonical sources, banned sources, definitions, style rules, and scoring rubric—then stacking only what the task needs. According to the X author, this approach is model-agnostic and scales for enterprise workflows, enabling safer AI-assisted drafting, faster review cycles, and clearer handoffs between roles.