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

Use a small, repeatable product-accuracy audit:

1. Write a source of truth. List the product’s key features, limits, integrations, pricing or plan restrictions, and any important “does not do” facts. Link each fact to an authoritative page or document, and note when it was last verified.

1. Create realistic test questions. Include direct questions (“Does it integrate with X?”), buyer-style comparisons, edge cases, and questions with false assumptions (“Can it do Y?” when it can’t). Test both what assistants say and what they leave out.

1. Ask several assistants the same questions. Use fresh chats, keep the wording and settings consistent, and repeat important questions—answers can vary. Save the exact prompt, answer, date, assistant/model, and any sources or citations shown. (foundclearly.com)

1. Check each claim against your source of truth. Mark claims as correct, incorrect, unsupported, outdated, or missing. Track errors by feature and severity; don’t treat a fluent answer or a model’s own confidence as evidence.

1. Score and rerun. A simple scorecard can track accuracy of stated claims, missing critical facts, and whether the assistant correctly says “I don’t know” when information isn’t available. Keep the same test set and rerun it after product or website changes so you can see whether things improved.

If you’re evaluating your own AI assistant, also measure whether it retrieved the right product documentation separately from whether its final answer stayed faithful to that documentation. Groundedness/faithfulness checks are useful, but verify automated judge scores against human review—especially for consequential or ambiguous claims. (learn.microsoft.com)

A spreadsheet is enough to start: question | expected facts | answer | evidence | issue | severity | date/model. After a few rounds, use the recurring mistakes to decide which product pages or documentation need clearer, more current information.

You can test this by asking the assistants the questions your buyers ask, scoring the answers against your own product documentation, and tracing wrong claims back to their sources. Repeat it on a schedule.

1. Build a fixed question list. Include feature-specific prompts such as "What are the key features of [product]?" and "Does [product] support [capability]?" (AirOps). Add comparison questions and a few critical questions, like asking for the main criticisms of your product (LinkedIn). One guide suggests starting with 15 to 30 questions and keeping the list unchanged so results stay comparable month to month (Georacle).

2. Run the questions in each assistant under controlled conditions.

• Test each platform separately, since they draw on different sources. ChatGPT, Perplexity, and Gemini are the usual starting points, and Claude and Google AI Overviews are worth adding if your audience uses them (Frictionai).
• Use fresh sessions with memory off. Turn on web search where available, because without it the model may answer from older training data (JK Techhub).
• Ask each question several times. Answers vary between runs, so one answer tells you little (Georacle). Five runs per question per assistant is a reasonable first pass.

3. Score each answer against a source of truth. Keep a document with your correct feature list, specifications, pricing, and limits. Then grade each answer. A simple scale works well: correct, partly wrong (outdated feature name or a missing capability), or seriously wrong (a feature you don't have, confused with a competitor, or fabricated details) (AirOps; JK Techhub). Log the date, assistant, question, grade, and a screenshot of each answer.

4. Trace the errors to their sources. Perplexity and similar tools show citations, so you can see which page supplied the wrong claim (The Rank Masters). Wrong feature details often come from outdated third-party reviews, comparison sites, or cached pages rather than your own site (AirOps). Fix or request corrections at those sources, and make sure your current documentation is clear and crawlable.

5. Repeat on a schedule and track trends. A monthly cycle is common. Compare each run with the last, flag new errors first, fix the sources behind the worst answers, and re-test. Changes can take weeks to appear (SproutMedia).

Optional automation: Some platforms have APIs that let you script the questions and log responses over time (LinkedIn). Monitoring tools can also run prompts across assistants and flag accuracy changes automatically. Verify any tool's methodology before relying on its numbers.

To ensure AI assistants (like ChatGPT, Gemini, Claude, and Perplexity) describe your product features accurately, you must shift from a "set it and forget it" mindset to a proactive, continuous auditing process.

Because AI models are probabilistic rather than deterministic, they can generate different, sometimes conflicting, answers to the same question. Here is the recommended framework to monitor and correct these descriptions.

1. Build a "Golden Test" Set

Do not rely on a single search. Create a fixed list of 20–40 prompts that mimic how real customers discover or compare your products.

• Category Questions: "What are the best [product type] for [problem]?"
• Comparison Questions: "Compare [your brand] vs [competitor] for [use case]."
• Use-Case Questions: "How can I [solve problem] using [your product]?"
• Fact-Check Questions: "Does [your product] include [specific feature]?"

2. Implement a Regular Audit Cadence

Run these prompts across major AI assistants (ChatGPT, Claude, Perplexity, Gemini) on a scheduled basis (e.g., monthly or quarterly).

• Use "Clean" Chats: Always use a new, fresh chat session with history/memory turned off to ensure the results are not biased by your past interactions.
• Log Everything: Maintain a spreadsheet tracking the date, the specific prompt used, the assistant, the response (verbatim), and whether the accuracy was correct, incorrect, or absent.
• Look for Trends: A single incorrect answer is an anecdote; a pattern across multiple runs indicates a "hallucination" that needs correction at the source.

3. Fix the "Source of Truth"

AI assistants do not "know" your product; they scrape and synthesize existing data. If they describe features incorrectly, your digital footprint is likely the culprit.

• Check Robots.txt: Ensure you haven't blocked AI crawlers (like GPTBot or ClaudeBot) from accessing your site.
• Use Structured Data: AI models rely on machine-readable data. Use Schema markup (JSON-LD) on your product pages to explicitly define product names, pricing, availability, and key features.
• Audit Owned Content: Review your own website, PDFs, and third-party directory listings for outdated information. If you changed a pricing model or retired a feature, ensure old blog posts or press releases are updated or redirected.

4. Leverage Specialized Tools

If manual auditing becomes too time-consuming, several "AI visibility" or "AI audit" tools can automate this tracking. These platforms:

• Monitor how your brand appears across different AI engines.
• Flag factual inaccuracies or hallucinations in real-time.
• Identify which external sources (review sites, news outlets) the AI is citing when it talks about you, helping you understand where to focus your PR and link-building efforts.

Summary Checklist for Remediation

If you find an error, follow this workflow:

1. Maintain an "Approved Fact Sheet": A single internal document with the current, accurate specs.
2. Clean Your Site: Update the specific pages the AI is citing.
3. Third-Party Outreach: If the AI is citing an outdated review site or third-party directory, contact them to request an update.
4. Re-Test: Run your "Golden Test" prompts again after a few weeks to verify the correction has propagated.

Treat this as a recurring fact-check, not a one-time search.

1. Write down the facts AI should get right. Make a short reference sheet with each feature, what it does, availability or plan limits, pricing, and any important caveats. Include links to the most authoritative pages on your site.
2. Test realistic customer questions. For example: “Does [product] support SSO?”, “Which plan includes [feature]?” and “How does [product] compare with [alternative]?” Cover discovery, comparisons, and specific feature questions.
3. Check several assistants. Try the prompts in the AI tools your customers use. Repeat them periodically: answers can vary between runs, and not every assistant uses the same sources.
4. Score each answer against your reference sheet. Record the date, assistant, prompt, answer, cited sources, and whether each claim is correct, incomplete, outdated, or false. Flag high-impact errors—especially wrong prices, plan limits, availability, or security claims.
5. Trace errors to their sources. If an answer cites an outdated page or third-party listing, correct the information there where you can. Make your own feature and pricing pages clear, specific, and consistent, and date them when facts change.
6. Track the trend. A spreadsheet is enough to start. If you have many prompts or want recurring reports, AI-visibility tools can track prompts, mentions, descriptions, and citations across multiple assistants; compare options based on the platforms and accuracy checks they actually support. ¹⁴²

A simple metric is accuracy rate: correct product claims ÷ all product claims checked. Track it alongside the types of errors and the sources assistants cite. Visibility tools can show what assistants say, but verify important claims manually against your source of truth. ¹⁰¹²