Expert attribution prompts for source pages

Summary

• This pack gives you six copy-paste prompts for expert bios, method notes, evidence, quotes, disclosures, and refreshes.
• Each prompt runs only on inputs you supply, such as {expert_name}, {verified_credentials}, and {interview_notes}.
• Every prompt carries a HOLD line that stops the model when an input is missing.
• The prompts make expertise clear and checkable, and no prompt guarantees AI citations or revenue.

Use these six prompts to add expert attribution that AI engines can check and cite. Each prompt turns verified facts into a page element that a human approves before publishing.

AirOps is an enterprise answer engine optimization (AEO) and content operations platform. We wrote this pack for a head of search engine optimization (SEO) who governs hundreds of expert pages. You get repeatable prompts that protect accuracy at scale, with clear stops for missing facts.

What expert attribution prompts do for AI citations

Expert attribution prompts make a page's authorship, method, and evidence explicit and verifiable. AEO is the practice of earning citations and mentions in AI answers. Citations are the source links an AI engine attaches to an answer.

Google's helpful content guidance asks you to show who created a page, how, and why. It asks whether bylines lead readers to background on the author. Google also calls fabricated creator profiles a form of deception, including made-up names and false credentials.

Google's quality rater guidelines define experience, expertise, authoritativeness, and trust (E-E-A-T). They name Trust the most important member. The prompts produce five proof elements that map to this guidance:

Inputs and hold gates to set before you run the prompts

The prompts are only as accurate as your inputs. Collect each input from its owner before anyone opens an AI tool.

Four habits keep the inputs clean:

• Get written consent from each expert before you publish their name.
• Verify every credential against a source a reviewer can open.
• Run prompts in an AI tool your security and legal leads approved.
• Name one human reviewer per page before drafting starts.

Google's generative AI guidance says to fact-check and review all AI-generated content before publishing. It also warns that mass-producing low-value pages can violate its spam policy on scaled content abuse. The HOLD line builds that review into each prompt.

Six prompts to add expert attribution that AI engines cite

Copy each prompt, swap the placeholders for verified inputs, and keep the HOLD line intact. No prompt guarantees a citation.

• Prompt 1 writes the expert bio block and author markup.
• Prompt 2 adds a method note and a review date.
• Prompt 3 inserts first-hand evidence from interview notes.
• Prompt 4 extracts approved quotes from a transcript.
• Prompt 5 flags connections that need a disclosure.
• Prompt 6 refreshes the block when the expert or data changes.

Role: Editor who writes expert bylines from verified facts only.

Inputs:
- Expert name: {expert_name}
- Verified credentials: {verified_credentials}
- Job title and employer: {job_title}, {employer}
- Author profile URL: {author_profile_url}
- Page URL: {page_url}

HOLD: Stop and return questions if any input is missing. Do not invent names, credentials, quotes, dates, or metrics.

Task:
1. Write a byline: "By {expert_name}, {job_title} at {employer}."
2. Write a 40 to 60 word bio from the supplied credentials only.
3. Link the byline to {author_profile_url}.
4. Return author JSON-LD with one Person object per author.
 Include name, url or sameAs, jobTitle, and affiliation.
 Put titles such as Dr. in honorificPrefix, never in name.
5. Confirm the markup matches the visible byline.

Output: byline, bio, JSON-LD, and the facts you used.

Role: Editor who writes plain method notes.

Inputs:
- Page URL: {page_url}
- Research method: {method_summary}
- Data sources: {data_sources}
- AI assistance used: {ai_assistance_note}
- Reviewer and review date: {reviewer_name}, {last_reviewed_date}

HOLD: Stop and return questions if any input is missing. Do not invent names, credentials, quotes, dates, or metrics.

Task:
1. Write a "How we made this page" note in 50 to 80 words.
2. Name each data source exactly as supplied.
3. State how AI helped, using {ai_assistance_note} as written.
4. Add "Reviewed by {reviewer_name} on {last_reviewed_date}."
5. Make no claim that the date improves rankings or citations.

Output: the method note and any vague inputs to tighten.

Role: Editor who adds first-hand evidence to a draft.

Inputs:
- Expert name: {expert_name}
- Interview notes with line numbers: {interview_notes}
- Approved metrics with sources: {approved_metrics}
- Current draft: {page_draft}
- Counsel-approved disclosure policy: {disclosure_policy}

HOLD: Stop and return questions if any input is missing. Do not invent names, credentials, quotes, dates, or metrics.

Task:
1. Find two to four first-hand examples in {interview_notes}.
2. Name the draft section where each example fits.
3. Write a 2 to 3 sentence insert for each.
4. Cite the note line after each insert.
5. Use only numbers from {approved_metrics}.
6. Mark any example the expert must re-confirm.

Output: a table of insert, section, note line, and status.

Role: Editor who selects quotable lines from a transcript.

Inputs:
- Expert name: {expert_name}
- Verified credentials: {verified_credentials}
- Transcript with timestamps: {interview_transcript}
- Quote approver: {quote_approval_contact}

HOLD: Stop and return questions if any input is missing. Do not invent names, credentials, quotes, dates, or metrics.

Task:
1. Pick three to five lines that answer a reader question.
2. Copy each line word for word, with no paraphrase.
3. Keep each quote under 40 words.
4. Add the supplied timestamp after each quote.
5. Attribute each as "{expert_name}, {verified_credentials}".
6. Label every quote PENDING until {quote_approval_contact} approves it.

Output: quotes with timestamps, attribution, and approval status.

Role: Reviewer who flags conflicts of interest for legal review.

Inputs:
- Expert name and credentials: {expert_name}, {verified_credentials}
- Relationships to the brand: {expert_relationships}
- Payment, free products, or equity: {compensation_details}
- Affiliate or tracked links: {affiliate_links}
- Current draft: {page_draft}
- Counsel-approved disclosure policy: {disclosure_policy}

HOLD: Stop and return questions if any input is missing. Do not invent names, credentials, quotes, dates, or metrics.

Task:
1. List every business, family, personal, or paid connection.
2. Mark each connection a typical reader would not expect.
3. Draft a clear disclosure sentence for each marked connection.
4. Place each disclosure next to the claim it affects.
5. Check that claimed expertise matches {verified_credentials}.
6. Use only the disclosure policy supplied by counsel as the review frame.
7. End with: "Not legal advice; confirm with counsel."

Output: marked connections, draft disclosures, and questions for counsel.

Role: Editor who updates expert attribution after a change.

Inputs:
- Page URL: {page_url}
- Current expert block: {current_expert_block}
- What changed: {change_event}
- New verified facts: {new_verified_data}
- Expert status (active, new role, departed, consent withdrawn): {expert_status}
- Reviewer: {reviewer_name}

HOLD: Stop and return questions if any input is missing. Do not invent names, credentials, quotes, dates, or metrics.

Task:
1. List every line in {current_expert_block} that is now wrong.
2. Draft replacements from {new_verified_data} only.
3. When the expert withdraws consent, list each name, bio, quote, and markup line to remove.
4. Say whether the change justifies a new visible date.
5. Route all edits to {reviewer_name} before publishing.

Output: a change list, replacement lines, and a date recommendation.

What expert attribution can and cannot do

Expert attribution makes your expertise legible and checkable. The sources cited here do not establish a causal citation effect for bios, credentials, or author markup. On schema.org, the author property accepts a Person or an Organization and defines vocabulary only.

Hold your own claims to the same standard:

• Present attribution as a trust practice, never as a citation guarantee.
• Describe E-E-A-T as a rater framework, never as a score.
• Limit author markup claims to Google Search.
• Keep evidence about search guidance separate from claims about AI citation performance.

Expert attribution is one input to a wider program. Our answer engine optimization hub maps the other inputs that shape AI answers.

How to run expert attribution prompts across a content library

Run the six prompts as one sequence, with a named owner at each step. Start with a small batch and expand once the review queue runs cleanly.

Measure citations and mentions as separate metrics after each release. Our guide to outcome-based AEO measurement shows how to tie page changes to business results.

• Start with pages that already earn AI citations or carry high commercial value.
• Store voice rules in your Brand Kit so every prompt draws on one source.
• Route every draft through one review queue with a named approver.

Key takeaways

• Verified inputs come first, because every prompt inherits the accuracy of its facts.
• The HOLD line converts a missing input into a question for a human.
• Google guidance supports clear authorship and method notes for Search.
• Citation and mention measurements belong alongside editorial quality checks, not in place of them.
• Disclosure and refresh checks protect trust long after a page launches.
• Shared rules keep bios consistent across brands, the core of multi-brand content governance.

Frequently asked questions

AirOps for expert attribution at scale

AirOps works as an extension of your team and leads with strategy across the channels that shape AI discovery. Our experts help you decide which pages need expert attribution first.

• Owned content, where expert bios, evidence, and quotes live on your pages.
• External content, where trusted third-party sources reference your experts.
• AI ads, community, and social and influencer channels that shape how AI describes you.
• Measurement that tracks citations and mentions as separate metrics.

The AirOps platform backs that strategy with intelligence and action. Brand Kit stores your brand voice and rules. Workflows run content updates, and AEO tracking reports citations and mentions.

Bring your priority pages and expert roster, and we will plan your first attribution sprint together.