What Is an AI Answer Audit?

An AI answer audit is a point-in-time, systematic review of what AI engines actually say about your brand: how they describe you, whether the facts are right, who they recommend alongside or instead of you, which sources they cite, and what tone they take. It answers the question every executive eventually asks — "what does ChatGPT say about us?" — with evidence instead of one anxious screenshot.

Why audit AI answers instead of just tracking metrics?

Metrics tell you how often you appear; an audit tells you what is being said. A brand can score well on mention rate while engines describe it with outdated positioning, recite retired pricing, or recommend it "for small teams only." Those qualitative findings drive different work than visibility numbers do: fact-sheet corrections, source-of-truth pages, PR targets. Audits also catch hallucinations before customers do — wrong integrations and invented features surface reliably under structured probing.

How do you run an AI answer audit?

The standard template has five steps:

1. Build the prompt matrix. Branded, category, comparison, and problem prompts — typically 30-80 prompts for a first audit.
2. Run each prompt across every engine, in clean sessions, several times each, capturing full answers and cited sources. Note browsing-on versus browsing-off behavior separately, since parametric and retrieved answers differ.
3. Score answers against a canonical fact sheet: factual accuracy, sentiment, position among competitors, citation presence.
4. Trace sources. For every claim about your brand, identify which page the engine leaned on — yours, a review site, a stale press article.
5. Compile findings into actions: facts to correct, pages to create or update, third-party sources to influence.

How does an audit relate to continuous monitoring?

The audit is the snapshot; continuous tracking is the movie. Most teams run a full audit quarterly and let automated monitoring watch the tracked prompt set in between, re-auditing early when an engine ships a major model update.

Example

A first audit for a mid-market HR platform found Gemini describing it accurately, ChatGPT citing a 2023 TechCrunch article for pricing, and Perplexity recommending it in only 2 of 10 category prompt runs. Three engines, three different problems, three distinct fixes — which is exactly why per-engine auditing beats assuming AI answers are uniform. Related terms are defined across the glossary.