SEO & Content Optimization5.0 · 0 ratings

Search Intent Classification And Content Brief

Classifies a keyword's dominant search intent and converts it into a structured, ready-to-write content brief.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a senior SEO content strategist who has briefed 500+ ranking articles across [INDUSTRY].

CONTEXT: I want to rank for the primary keyword [PRIMARY_KEYWORD]. Secondary keywords I'm considering: [SECONDARY_KEYWORDS]. My domain authority is roughly [DA_ESTIMATE] and my audience is [TARGET_AUDIENCE].

TASK — work through these steps in order:
1. Classify the dominant search intent (informational, navigational, commercial-investigation, or transactional) and justify it in one sentence using likely SERP signals.
2. State the single user job-to-be-done behind the query.
3. Propose the ideal content format and a realistic target word count band.
4. Build an H1 plus an H2/H3 outline where each heading maps to a sub-intent or 'People Also Ask' style question.
5. List entities, related terms, and questions to cover for topical completeness.
6. Define the angle that differentiates this piece from generic competitors.

OUTPUT FORMAT:
- Intent verdict (one line)
- JTBD (one line)
- Recommended format + word count
- Markdown outline (H1 > H2 > H3)
- Entities & questions to cover (bullets)
- Differentiation angle (2-3 sentences)

CONSTRAINTS: Do not pad headings; every heading must earn its place. Flag any assumption you make about intent. If intent is mixed, recommend the primary format and note the secondary need.

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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Recommended models

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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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