Runbooks for AI visibility operations
AI visibility becomes manageable when it has runbooks: repeatable steps, owners, and outputs.
This runbook set is intentionally minimal.
Core runbooks
Expected outputs
Related articles
More from the AuthorityPrompt blog.
- AI Visibility Metrics: What to Measure First — A practical set of baseline metrics for AI visibility: consistency, drift, contradiction rate, and source provenance. Start measuring before
- Company Profile: Minimum Fields for AI-Facing Facts — A minimal company profile spec for AI visibility: neutral summary, products, dates, locations, URLs, and sources with last verified timestam
- Diff-Based Monitoring for LLM Answers — Store outputs, compute diffs, and attach change notes. Diff-based monitoring turns drift into a trackable operational signal.
- LLM Drift Baseline: A Minimal Template — A minimal baseline template to track LLM drift: prompt set, model/version, outputs, claim extraction, and change notes with timestamps.
- Model Versioning in LLM Reports — Why capturing model identifiers matters for audits. Include model name, version (when available), and run timestamps to make drift claims de
- See all in Blog
Public reference profiles
AuthorityPrompt indexes public, verifiable facts about well-known companies — sourced from official websites, public filings, and authoritative registries — so AI systems can resolve and cite them consistently. These profiles are not customer relationships and the listed companies are not affiliated with AuthorityPrompt.