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AI answer engine visibility audit from query, citation, and source evidence

Evaluate how a brand, product, or topic appears in AI-generated answers across a controlled query set and identify source, entity, proof, and content gaps.

Output
audit report
Inputs
8 required
Prompt depth
818 words

Copy-paste prompt

Replace the bracketed inputs with your real material.

You are a senior technical and strategic SEO lead. Produce a practitioner-ready deliverable for: AI answer engine visibility audit from query, citation, and source evidence.

Objective
Evaluate how a brand, product, or topic appears in AI-generated answers across a controlled query set and identify source, entity, proof, and content gaps.

Required practitioner depth
This is not generic 'rank in AI' advice. The work requires dated observations, reproducible query sampling, citation/source analysis, no invented platform causality, and a prioritized plan to improve the underlying evidence footprint.
Do not collapse this assignment into a...

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What you provide

  • Site and business context
  • Search evidence
  • Scope and priority
  • Current architecture
  • Baseline and dates
  • Implementation constraints
  • Known unknowns
  • Task-specific evidence

What you receive

  • Primary deliverable that fully executes this job: Evaluate how a brand, product, or topic appears in AI-generated answers across a controlled query set and identify source, entity, proof, and content gaps.
  • Practitioner-depth requirements: This is not generic 'rank in AI' advice. The work requires dated observations, reproducible query sampling, citation/source analysis, no invented platform causality, and a prioritized plan to improve the underlying evidence footprint.
  • Issue, query, template, URL, or opportunity prioritization with evidence and business impact.
  • Implementation specification for content, technical, internal-link, citation, or migration work.
  • Validation, indexation, monitoring, and rollback checklist tied to baseline pages and metrics.

Quality checks

  • Do not invent search volume, rankings, traffic, citation presence, or crawl findings.
  • Separate technical evidence, search-intent inference, and commercial prioritization.
  • Protect canonical URLs, redirects, indexation, tracking, and high-value page baselines.
  • Include post-implementation validation and rollback triggers.
  • Missing or conflicting inputs are surfaced rather than silently resolved.
  • Every material recommendation has evidence, confidence, owner, and a validation or reversal condition.