Expertise is whether AI describes your features, specifications, and capabilities accurately. It is the ANSWER category for AI accuracy, and a wrong answer here disqualifies you on a requirement you actually meet. This playbook is the practical guide to fixing what AI gets wrong. For the background, see the Expertise pillar guide.
The principle is simple: an engine can only describe your product as accurately as its sources allow. So improving Expertise is the work of giving engines clear, current, factual source material about what you do. This is answer engine optimization (AEO), also called generative engine optimization (GEO), applied to your capabilities. One caution before the tactics: keep it truthful. The accuracy cap in scoring means an answer built on an inflated claim is not counted as a win, so the goal is correctness, not exaggeration.
1. Find Out Exactly What AI Gets Wrong
You cannot fix an error you have not seen. Run capability prompts about your real features, integrations, and specs, logged out, in your target markets, across ChatGPT, Perplexity, and Google's AI answers. For each, record whether the answer is accurate, incomplete, outdated, or invented, and capture the specific error. The pattern is usually concentrated: a few wrong facts, often the same ones, repeated across engines because they trace back to the same stale source.
2. Publish Plain, Factual Capability Content
The most common reason AI misstates your product is that your own pages describe it in marketing language an engine cannot map to a concrete capability. State your features, integrations, and specifications in plain terms, the way a customer would ask about them. A page that lists "native integrations with Salesforce, HubSpot, and Slack" is easy for an engine to repeat correctly. A page that promises "seamless connection to the tools you love" is not.
3. Keep Specs and Integrations Current Everywhere
A large share of Expertise errors are simply outdated facts. If you shipped a feature last quarter but your docs, listings, and third-party profiles still describe the old product, the engine will keep judging you on it. Treat your capability information as something to maintain, not publish once. Update specs, integration lists, and limitations across your own site and the directories and profiles an engine reads whenever the product changes.
4. Structure Content So It Is Easy to Extract
Engines extract facts more reliably from clear, well-structured content than from dense prose. Specification tables, labeled feature lists, and direct question-and-answer formats make the correct fact easy to lift and hard to misread. The clearer the structure, the less room a model has to fill a gap with a plausible-sounding guess, which is where invented limitations and AI hallucination come from.
5. Earn Citations to Your Own Documentation
When engines cite your own domain as the source for a capability, accuracy improves and you can see it in the audit's Citation Rate. Make your documentation public, linkable, and authoritative, and earn references to it from credible third parties. The more the engine leans on your own current docs rather than stale secondhand sources, the more accurate its answers become.
6. Correct the Wrong Sources, Then Re-Measure
When an audit pins an error to a specific source, an old review, a wrong directory entry, an outdated comparison, fix it at the source where you can, and publish a clear, correct counter-source where you cannot. Then re-run the same capability prompts on a regular cadence, because product facts change and new errors appear as you ship.
Where the Work Gets Hard
Any single fact is easy to correct. The difficulty is finding every error, across every engine and market, tracing each to its source, and re-checking after each release. A handful of spot checks barely scratches it, because answers vary from ask to ask and errors hide in specific phrasings. Running the full set properly is a real undertaking, which is the honest reason most teams have it measured for them even when they fix the content themselves.
A HiBot audit gives you the baseline, your Accuracy Rate, and the ranked list of specific errors, so your effort goes where it moves the score the most. You can pressure-test your own product facts with The Capability Accuracy Audit, and see the full arc in Setting the Record Straight on Capabilities. Download the ANSWER whitepaper to see the method, or request an AI visibility audit at hibot.com.
