How do I check whether AI assistants describe my product's features correctly?

Treat it as a repeatable accuracy audit, not a one-off “ask ChatGPT” test.

1. Write down the source of truth. Make a compact, dated fact sheet for each important feature: what it does, limits, compatibility, availability, pricing or plan requirements, and what it doesn’t do. Link each fact to an authoritative product page or internal documentation.

1. Build a test set of realistic questions. Include common buyer questions, tricky edge cases, comparisons, and questions with false assumptions—such as “Does it work offline?” when it doesn’t. Test wording variations, not just one carefully phrased prompt.

1. Check several assistants and conditions. Run the questions in the assistants your customers use, both with fresh chats and, where relevant, with web search enabled. Record the date, assistant/model, settings, prompt, full answer, and any cited sources. Answers can vary, and current features may change.

1. Score the answers claim by claim. Label each product claim correct, incorrect, unsupported, or missing/unclear against your fact sheet. Track especially serious errors—such as invented capabilities, wrong plan limits, or outdated availability—separately from minor omissions.

1. Review the scoring yourself. A second AI can help flag claims, but don’t make it the final judge: automated graders can miss subtle errors. For a formal evaluation, keep a human-checked set of examples and compare any automated grader against it. Evaluation guidance similarly recommends representative examples, edge cases, and measurable criteria. (developer.apple.com)

1. Repeat on a schedule and after changes. Rerun the same questions after product, pricing, or documentation updates, and periodically even when nothing changes. Compare accuracy over time and investigate recurring errors.

A simple tracker can have columns for question, assistant/date, answer, claims checked, result, severity, source, and follow-up. Your most useful headline metrics are the share of claims that are correct, the share that are wrong, and the rate of serious errors—not just whether an answer “sounds right.” Generative models can confidently produce unsupported information, so checking claims against your own authoritative facts matters. (nvlpubs.nist.gov)

If you want, I can help you turn your feature list into a small test set and scoring rubric.

Start with a fixed set of questions, run them on the major assistants, and compare each answer against a verified fact sheet for your product.

1. Write a fact sheet of ground truth. List your core features, supported integrations, pricing, limits, availability, and anything you've recently changed or discontinued. Each AI claim will be checked against this document, so keep it current.

2. Build a prompt set that mirrors real buyers. Aim for about 10 to 15 prompts across a few types:

• Direct brand questions, such as "What do you know about [Your Product] and what does it do?"
• Category searches where you should appear, such as "best tools for [your use case]."
• Comparisons, such as "[Your Product] vs. [Competitor]."
• Critical or skeptical questions, such as "What are the main criticisms of [Your Product]?"
• Specific feature questions, such as "Does [Your Product] support [feature]?"

One source suggests that direct brand queries reveal whether the AI has any information about you at all, and that a "don't have information" reply signals a visibility gap. These reveal whether AI models have any information about you and how accurate that information is. If an AI model responds with "I don't have specific information about that company," you've identified a critical visibility gap.

3. Run the prompts on several assistants. One guide recommends testing across ChatGPT, Claude, Perplexity, and Gemini at minimum. Add Copilot and Google AI Overviews if your buyers use them. Record the full response, the date, and the model or version when you can. Answers vary between runs, so repeat important prompts a few times and on different days.

4. Grade each answer. For every response, mark each factual claim as correct, wrong, outdated, missing, or invented. One rubric weights factual accuracy most heavily and scores each response on a scale. Every claim in the response should be verifiable against your product data, policies, or order system. You can also score sentiment and whether competitors are framed better than you.

5. Automate the monitoring. Once you have a baseline, you can stop checking by hand. Some teams schedule recurring checks of AI answers with page-monitoring tools, while others use platform APIs to run prompts in scripts and log the results. Some AI platforms offer APIs that allow automated testing. You can build scripts to run queries programmatically and track responses over time. Also sample a set of responses regularly, such as a few dozen per week, and compare them against your catalog.

6. Fix the source, then re-test. Most wrong answers trace back to thin, outdated, or ambiguous public information. Publish clear, authoritative, well-structured content stating the correct facts, ensure your own pages are extractable and consistent, and use any vendor feedback channel to report the error. Then rerun the same prompts to see whether the answers change. Keep in mind that assistants that lack live data access may guess, so the freshness of your public pages matters.

Tip: keep the same prompt set and grading rubric for every run. Without that consistency, you can't tell whether a change in an answer reflects a real improvement or just different wording.

Checking whether AI assistants accurately describe your product features requires a proactive, systematic approach. Because AI outputs can vary from one interaction to the next, a single check is insufficient.

1. Perform Manual "Secret Shopper" Audits

The most direct way to understand how AI perceives your brand is to act like a customer.

• Use Diverse Prompts: Ask the same questions your potential customers ask. Cover different categories: problem-based ("How do I fix [issue] with [product type]?"), comparison-based ("Compare [Your Product] with [Competitor]"), and intent-based ("What are the best [product type] under [price]?").
• Test Multiple Platforms: Do not rely on just one AI. Run your tests across major engines including ChatGPT, Google AI Overviews, Perplexity, Claude, and Grok. Each has different retrieval behaviors and sources.
• Maintain a "Clean" Testing Environment: Use private or incognito browser windows to avoid personalized search history influencing the results. Record the platform, the exact prompt, the date, and whether your brand was mentioned, recommended, or described accurately.
• Look for Trends, Not Anecdotes: Track these results in a spreadsheet over weeks. If you see recurring inaccuracies, you have identified a specific information gap that needs to be corrected at the source.

2. Audit Your Technical "AI Readiness"

AI assistants often "read" your product data via machine-readable formats rather than your visual website layout.

• Check Structured Data: Ensure your website uses schema markup (JSON-LD) to clearly define product names, prices, availability, and features. Tools like Google’s Rich Results Test can show you exactly what data "machines" see when they crawl your page.
• Review robots.txt: Ensure you have not accidentally blocked AI crawlers (like GPTBot, Claude-Web, or Google-Extended) from accessing your site.
• Optimize for Clarity: AI models predict the "likely next word." If your product pages contain blank descriptions, outdated specs, or ambiguous language, the AI is more likely to hallucinate or omit your product. Use clear, concise, and factual descriptions.

3. Leverage Dedicated Monitoring Tools

If you have a large product catalog, manual checking is not scalable.

• AI Visibility Platforms: There is a growing category of tools—such as AIclicks, Slate, or Foglift—designed specifically to monitor brand mentions, citation accuracy, and competitive positioning across multiple AI engines.
• Automated Tracking: For higher volumes, you can use scripts or API-based tools to run a fixed set of prompts on a weekly schedule. These tools log responses and help you build an audit trail of how your brand’s representation evolves over time.

4. Maintain a "Single Source of Truth"

Ultimately, AI accuracy is a reflection of the data you provide to the web.

• Regular Data Hygiene: Treat your product data as a living asset. If you update a feature or price, ensure that change is reflected in your structured data, on your website, and on third-party marketplaces or review platforms where AI might pull its information.
• Fact-Check Regularly: If you use AI to generate any of your own marketing content, manually verify every number, name, and claim before publishing. Unchecked AI-generated content can create a feedback loop where AI models "learn" your own hallucinations as facts.

Summary Checklist for Your Team

• [ ] Monthly Audit: Run a consistent set of ~20–40 "buyer intent" prompts across ChatGPT, Perplexity, and Google AI Overviews.
• [ ] Technical Review: Use Schema/Structured Data to ensure your product specifications are machine-readable.
• [ ] Competitive Benchmarking: Track your "Share of Voice" in AI responses relative to your top 3–5 competitors.
• [ ] Data Maintenance: Establish a process to update technical specs and product descriptions immediately upon any product launch or change.

Treat it like a repeatable factual audit—not just a check of whether assistants mention your product.

1. Create a source of truth. List each important feature and its exact facts: what it does, limits, compatibility, pricing or plan availability, and any relevant qualifiers. Link each fact to the current product page or documentation, and note when it was last checked.

1. Test realistic questions. Ask several assistants the kinds of questions customers might ask, including:
   • “What does [product] do?”
   • “Does it support [specific feature or integration]?”
   • “How does it compare with [alternative]?”
   • “Which plan includes [feature]?”
   • “Is it suitable for [use case]?”

Include direct product questions and broader category or comparison questions; assistants may describe you differently depending on how the question is phrased.

1. Save the evidence. Record the exact prompt, assistant, date, locale, and full answer, including any citations. Answers can vary, so repeat important tests and keep the original wording.

1. Check claims one by one. Compare each factual statement with your source of truth. Mark it correct, incorrect, outdated, or unclear. Pay particular attention to feature availability, limits, integrations, pricing, and compatibility. A favorable description can still be inaccurate.

1. Look for patterns and likely causes. If an answer cites a page, check whether that page really supports the claim and is current. Review your own site and docs for conflicting descriptions, stale pages, duplicate PDFs, or missing details. Assistants may also rely on third-party sources, so note those separately.

1. Fix and retest. Correct the pages or documentation you control, then rerun the same prompts over time. Track which errors recur and how quickly they disappear. AI-visibility monitoring tools can help automate this, but verify any tool’s findings against the saved answers and source material; mention counts alone don’t measure accuracy. ⁴⁶⁹

A simple tracker can use: prompt | assistant | date | answer | claim | source/evidence | status | follow-up.