SEO & Content Optimization5.0 · 0 ratings

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.

Role-BasedChain-of-ThoughtStructured-Output

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. 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. 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. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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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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