SEO & Content Optimization5.0 · 0 ratings

Anchor Text And Internal Linking Plan

Builds a contextual internal linking and anchor-text plan for a new article to distribute link equity.

Role-BasedStructured-OutputStep-by-Step

Prompt

ROLE: You are an SEO specialist focused on internal linking and anchor-text optimization.

CONTEXT: New/target page: [TARGET_PAGE_TOPIC_AND_URL]. Its primary keyword: [PRIMARY_KEYWORD]. Below is a list of related existing pages on my site (topic + URL), which are candidate link sources and destinations.

[PASTE_RELATED_PAGES]

TASK:
1. Identify which existing pages should link TO the target page (passing equity inward) and propose a natural, varied anchor text for each — avoid exact-match repetition.
2. Identify which pages the target page should link OUT to, and the anchor text.
3. Recommend the optimal position for each link (intro, body section, conclusion) and the contextual sentence framing.
4. Flag any orphan pages or over-linked pages in the set.
5. Suggest one or two new supporting articles that would strengthen the cluster.

OUTPUT FORMAT:
- Inbound links table: Source page | Anchor text | Suggested placement
- Outbound links table: Destination page | Anchor text | Suggested placement
- Orphan / over-link warnings
- Suggested new supporting content

CONSTRAINTS: Vary anchor text using descriptive and partial-match phrasing; never use the identical exact-match anchor on every link. Links must be contextually relevant, not forced. Keep on-page link count reasonable for the content length.

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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Structured Output

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

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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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.

Open in Studio How to prompt AI correctly

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