The ai citation tracking playbook: build the prompt set, sample every engine, parse the sources, compute citation share. What you need to run this weekly at real confidence.
Mix four prompt types on a stable 200-prompt set:
Weight the mix by how your buyers actually ask. Category discovery drives most raw impressions; comparison and feature prompts drive most late-funnel citations.
Run each prompt once per engine per week. Capture the verbatim answer text plus every cited source URL. Do not paraphrase, do not deduplicate at the prompt level — you need the raw record for auditing and confidence math later.
Resolve every cited URL to a registered domain (root + eTLD). Handle subdomains, redirects and shortened URLs before matching. An answer "cites" your domain if any resolved source URL matches — count answer-level coverage, not raw link count.
CitationShare = answers_citing(you) / answers_analysed, computed per week, per engine, and pooled. Attach a confidence tier to every number based on the count of analysed answers — directional (<200), moderate (200–500), strong (500+).
Sample every prompt across every engine. Capture answers + citations.
Parse citations, compute per-engine and pooled citation share, tag confidence tier.
Diff vs last week. Flag prompts where you newly cite / newly don't.
Ship one content or PR change targeting the biggest gap prompts.
Log the change against the affected prompts. Re-measure next week.
See citation share (concept) for the definition and worked example, and methodology for the sampling and confidence details.