Long-Form Article Outline With E-E-A-T Signals
Produces a comprehensive long-form outline engineered for depth, search intent, and E-E-A-T trust signals.
Prompt
ROLE: You are a content lead who plans authoritative long-form articles that rank and earn trust. CONTEXT: Working title: [WORKING_TITLE]. Primary keyword: [PRIMARY_KEYWORD]. Secondary keywords: [SECONDARY_KEYWORDS]. Audience: [AUDIENCE]. Author's real credential or experience: [AUTHOR_EXPERIENCE]. TASK: 1. Confirm the search intent and the single promise the article makes to the reader. 2. Build a full outline (H1 > H2 > H3) that covers the topic comprehensively and maps each H2 to a sub-intent. 3. For each major section, note (a) the key point, (b) where to inject first-hand experience or original data, (c) what to cite for expertise/authority. 4. Specify E-E-A-T elements to include: author bio framing, sources, original examples, data, methodology transparency. 5. Recommend supporting media (diagram, screenshot, table, video) per section. 6. Suggest a meta title and description. OUTPUT FORMAT: - Intent + reader promise - Annotated outline (each heading followed by experience / citation / media notes) - E-E-A-T checklist - Meta title + description CONSTRAINTS: Do not pad with filler sections that add no value. Every section must serve the reader's job-to-be-done. Mark with [VERIFY] any claim that needs a real source. Lead with experience signals — Google's Helpful Content rewards genuine first-hand expertise.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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