Master a structured framework for identifying, categorising and auditing brand citations across major Large Language Models to benchmark visibility and site-source attribution.
12 min read
Foundations
Visual diagram
A workflow diagram showing the 4 stages of a citation audit: Discovery (Prompts), Extraction (AI Output), Categorisation (Data Normalisation), and Gap Analysis (Insights).
Section 1 of 8
Introduction
In the era of Generative Engine Optimisation (GEO), a brand's visibility is no longer measured solely by blue links. Instead, the metric of success is the frequency and quality of 'citations'—the references AI models provide to validate their claims. Running a citation audit is the process of systematically cataloguing where LLMs (Large Language Models) like ChatGPT, Claude, and Gemini are sourcing their information and how often your brand (or your client’s brand) is being featured.
This lesson provides a repeatable, data-led methodology for capturing these citations at scale. Unlike traditional SEO audits that rely on crawler data, citation audits require a mix of prompt engineering, sentiment analysis, and source attribution tracking to understand why a model chooses one source over another.
Introduction
Lesson Quiz
Pass at 70%.
1. What is 'Citation Leakage' in the context of an AI audit?
2. Which prompt type focuses on users looking for general industry knowledge?
3. Why is it important to audit multiple AI models like Gemini and Claude simultaneously?
4. In a citation audit, what does 'Earned Media' refer to?
5. What is a recommended sample size for a representative citation audit?
6. What tool would you use to scale citation extraction from models that lack an API?
7. If your site ranks #1 in Google but has 0 citations in LLMs, what is a likely cause?
8. Which of these is an 'Implicit Reference'?
9. A 'Compare' prompt usually falls into which category?
10. What is the final phase of the Citation Audit Framework?
