Fix Wrong Information in AI Answers: A Practical Guide

Wrong information in AI answers about your prices, plans or features usually comes from either a model’s stored knowledge or pages it finds through live search. You cannot directly edit a chatbot’s answer, but you can trace its sources, correct outdated information and make your current facts easier to verify. This guide gives SaaS and e-commerce teams a practical way to do that.

Start by recording the exact incorrect claim and checking whether the answer cites a source. Then test with live search on and off, correct the strongest outdated sources, and retest over time. If you are responsible for a SaaS brand, SearchFIT’s SaaS industry resources can help you plan the wider work of improving how your brand appears in AI search.

Why AI answers get your pricing or features wrong

AI assistants do not have direct access to your internal product database, billing system or release calendar. When an answer states an old price, misses a feature or mixes up product tiers, it is usually drawing on information that is incomplete, outdated or about a different entity. As examined in Coyle Emerald Narratives' insights on ChatGPT software pricing errors, outdated pricing often spreads when models ingest legacy marketing pages or discount announcements.

There are two useful starting points for diagnosing the problem:

• Stored model knowledge: Information learned during model training. It may not reflect your latest pricing page or product release.
• Live retrieval: Information gathered from web pages or other sources while answering a prompt. Search results can surface your old blog post, an outdated review, or a cached page instead of your current product details.

Some errors involve both. A model may have learned an old fact and then find a web page that appears to confirm it. It can also confuse similar product names, plan labels or company entities. A response can vary between runs, so treat one answer as a clue to investigate rather than proof of a single cause.

A 2026 MaxAEO Tracking Report, described in its guide to incorrect company information in ChatGPT, classifies B2B brand-fact errors as retrieval errors, training-data or cutoff errors, and entity conflation. That breakdown is useful as a diagnostic framework. It does not mean every brand will see those causes in the same proportions.

The practical implication is simple: do not begin by rewriting your whole homepage or sending a support request to every AI company. First find out what the assistant is repeating and where that information might have come from.

What you need before you start

Set aside a shared document or spreadsheet for the investigation. A simple record prevents different teams from testing different prompts, overlooking old sources or declaring success after one good answer.

For each issue, collect:

Ask product, sales, support, legal and e-commerce teams to confirm the approved facts before publishing corrections. Mark whether a price is monthly or annual, per user or per account, introductory or ongoing, and inclusive or exclusive of tax. For product features, note the plan, region, release status and any prerequisites. An answer can appear wrong when it has omitted one of these conditions.

For retail products, include the exact product identifier, size or variant, availability and the market you serve. For SaaS, distinguish between a feature being available, being in beta and being available only on a particular plan. These details matter because assistants often compress information into short summaries.

Agree on a source of truth before making changes. Usually this is a current pricing or product page that the business owns and maintains. Your pricing page is one example of a place to review when assessing a brand’s public pricing information. The important point is to designate one canonical page for your own product facts and keep it accurate.

Step 1: Capture and reproduce the error

Start with the prompt that revealed the problem. Save the full response, not just the sentence that looks wrong. Record the date, assistant, whether web search was available, and any citations. If possible, save a screenshot as well as the source URLs, since answers and cited sources can change.

Then run a small set of related prompts. Keep the wording neutral and specific. For example:

• “What are the current plans and prices for [Brand] in Australia?”
• “Does [Brand] support SAML SSO, and which plan includes it?”
• “What is the return policy for [Product] bought from [Brand]?”
• “Is [Product] currently in stock in [market]?”

Avoid leading questions such as “Why does [Brand] still not offer SSO?” The assistant may adopt the premise rather than verify it. Ask for sources when the tool supports citations, but remember that a citation does not guarantee that the linked page supports every part of the answer.

Run each prompt in a fresh session, and do not paste the correct answer into the conversation first. Otherwise, the assistant may repeat what you supplied rather than reveal what it would say to a prospective customer. You can also test variations in wording, since a buyer may ask about the same fact in several ways.

Separate factual errors from differences in interpretation. “The Pro plan costs $X per month” is testable. “The best plan for a growing team is Pro” depends on the buyer’s needs. Prioritise specific factual claims, especially those about price, availability, security, eligibility or product capability.

Tip: Keep a verbatim copy of the wrong answer. Do not tidy its wording. Search for distinctive phrases from it later, and use the original language when comparing future results.

Mistake to avoid: Testing only the exact prompt that first exposed the issue. A response may change with a slightly different question, market or product variant. Your aim is to understand the pattern, not find one favourable answer.

Step 2: Compare search-on and search-off answers

Now test whether the answer changes when the assistant can search the web. This is a diagnostic, not a perfect way to inspect a model’s internal processes. Product interfaces differ, and a “do not browse” instruction may not guarantee that a tool has been disabled.

Use the same neutral question in two conditions:

1. Search on: Use an assistant or mode that shows web sources. Ask for the current fact and request citations.
2. Search off: If the product offers a genuine search-off mode, use it. Otherwise, ask for an answer from the model’s existing knowledge without browsing, and record that this is an approximate test.

Compare the claim, detail and sources. Interpret the results as clues:

Do not assume that an answer produced with search off comes solely from training data. Models can have other context, and testing controls are not identical across products. The aim is to decide where to look first, not to claim certainty about what happened inside the system.

You also cannot reliably force an immediate change to a model’s stored knowledge by editing a web page. A correction may become visible in a live-search answer before it affects a model that answers without browsing. Set expectations accordingly: fixing a source is actionable, while the timing of changes to model knowledge is outside your direct control.

Tip: Record the interface and settings, not just the model name. “ChatGPT” alone does not tell you whether a response used web search or which search experience produced it.

Mistake to avoid: Treating one search-off response as proof that an error is permanently baked into a model. Results can vary, and the mode may not have been fully isolated.

Step 3: Trace the claim to its sources

Review every citation attached to the answer. Open each page and check whether it actually supports the claim, whether the information is current, and whether it is about your product, plan and market. A source can be accurate in one context and misleading when the assistant applies it to another.

Then search for the exact words and details yourself. Search your domain for old prices, plan names and feature statements. Queries such as site:yourbrand.com "$29", site:yourbrand.com "not supported" or a distinctive sentence from the AI answer can expose pages that are easy to overlook. Search document titles and PDFs too. If you have access to Search Console or another site-search tool, use it to find pages receiving impressions or visits that still mention legacy information.

Check these first-party sources:

• Old pricing announcements, launch posts and plan comparison pages
• Help centre articles, FAQs and troubleshooting pages
• Product release notes, changelogs and archived documentation
• Downloadable PDFs, pitch decks and sales sheets
• Localised pages, regional storefronts and currency-specific pages
• Staging or campaign subdomains that remain publicly accessible
• Pages that say a feature is unavailable without explaining that the statement is historical

Next, inspect the third-party sources that appear in citations or search results. These may include software review sites, partner directories, affiliate comparisons, forums, product listings and news coverage. Search for the incorrect fact in quotation marks and look at the pages ranking for brand-plus-price or brand-plus-feature queries. Do not assume a source is important just because it is a familiar website. Prioritise what the assistant cites and what searchers can actually find.

A useful audit record includes the URL, the incorrect wording, the publication or update date if shown, whether you control the page, the correct replacement, and the action needed. Classify the source as current and accurate, outdated, ambiguous, or about a different plan or product.

The MaxAEO article linked above discusses retrieval as one possible source of business-fact errors. For a second practical perspective on tracing incorrect company answers, see AirankLab’s guide to fixing wrong AI answers about your brand. Treat these guides as starting points for your own source investigation, not as proof that a particular page caused your specific error.

Tip: Look beyond the first citation. An answer may combine several sources, or cite a current page while repeating a detail found elsewhere.

Mistake to avoid: Assuming the page with the most visible prominence on your site is the only source the assistant will use. A legacy article, PDF or third-party listing may be easier to retrieve or may state the disputed fact more explicitly.

Step 4: Correct first-party pages without creating new confusion

Once you have confirmed the current facts, update the pages you control. Make one clearly maintained page the canonical reference for each important subject, such as pricing, plans, security, returns or product specifications. Link to it from relevant pages using descriptive text, not vague phrases such as “learn more”.

For an old article that still has useful history, do not silently rewrite it as if the new information had always been true. Add a clear note near the top explaining that it describes a past offer, launch or product version, and link to the current page. For example:

This article describes our previous pricing structure. For current plans and prices, see our pricing page.

Use a date only when you have verified it. If a page is obsolete and provides no continuing value, consider whether it should be updated, redirected to a relevant current page or removed. Before changing a URL, check its traffic, backlinks, internal links and role in customer support. A redirect should lead to a genuinely relevant destination, not a generic homepage.

Review help content for absolute statements that have become stale. Replace “We do not support [integration]” with a current, precise statement if the integration is now available. For example: “The [integration] is available on the Enterprise plan. See the integration guide for setup requirements.” Include the applicable plan and limitations. Avoid replacing an old absolute with a vague claim that could create another misunderstanding.

Make facts easy to find and compare. Put prices, billing periods, feature availability and conditions in readable page text. Use clear headings and tables where they help visitors. Important details should not exist only inside an image, a downloadable document, a modal that is difficult to crawl, or text that appears only after a complex interaction. Test the page on mobile and check that the relevant information is present in the rendered page.

For SaaS, specify which plan includes each feature and whether limits apply. For e-commerce, make product details consistent across the product page, variant selector, shipping information and returns policy. Prices should clearly state currency and billing context. If prices differ by country, show the market-specific terms instead of presenting a single number as universal.

Keep an editorial record of material changes. Note who confirmed the fact, when it became effective and which related pages were updated. This helps support and sales teams answer questions consistently and makes later audits faster.

Tip: Put the current answer in plain language near the top of the relevant page, then provide detail below. A visitor should not need to interpret a long announcement to find today’s price or feature status.

Mistake to avoid: Deleting every page that mentions an old fact without considering its usefulness. A dated page with a clear explanation and a link to the current source can be less confusing than a broken link or a rewritten historical announcement.

Step 5: Make structured data support the visible facts

Structured data is information formatted for software to interpret. Product and offer markup can describe details such as a product name, price, currency and availability. It can help search systems understand a page, but it is not a way to force an assistant to use your preferred answer.

If you use Schema.org markup, check that the data matches what a customer can read on the page. Review the relevant Product and Offer properties, including currency and availability where applicable. Use the correct values for the product and market, and validate the markup with suitable testing tools. If a price is shown as “$50” on the page but the markup describes a different amount or currency, you have created conflicting signals rather than a correction.

Structured data should support accurate page content, not replace it. Do not place a crucial price or feature only in JSON-LD and assume that every AI search experience will extract and use it. Keep the fact in visible, readable text as well. Search engines and assistants change how they retrieve and interpret pages, so avoid claims that a specific markup field guarantees inclusion in an AI answer.

For online stores, also check your product feed and merchant listings. Make sure product identifiers, variants, prices, currency and availability agree with the storefront. If your business uses Google Merchant Center or another marketplace feed, review its diagnostics and update schedule. A stale feed can conflict with the product page, while a correct feed cannot compensate for a misleading page or another incorrect source.

An llms.txt file is sometimes proposed as a way to guide AI systems to important pages. If you choose to publish one, treat it as a supplementary directory that points to useful, current pages. Do not rely on it as a universal standard, a guaranteed crawler instruction or a substitute for making your website accessible and accurate. Put the core facts on the canonical pages first.

For a deeper explanation of the role and limits of structured information, see Omnius's breakdown of schema markup for AI search. The central rule for remediation remains straightforward: keep the human-visible page and any machine-readable data in agreement.

Tip: Recheck structured data after a pricing or catalogue update, especially if it is generated by a plug-in, product feed or separate content system.

Mistake to avoid: Adding markup to signal a feature, price or availability that customers cannot verify on the page. Structured data should describe the real offer, not make a stronger claim than the visible content.

Step 6: Ask third parties to correct outdated information

You can directly edit your own website. You generally cannot edit a review site, affiliate article or forum post yourself. Instead, identify the right owner or correction channel and provide a concise, verifiable update.

Start with third-party pages that are cited by AI answers or rank prominently for searches about your brand and the incorrect fact. Contact the publisher or platform with:

• The exact URL and statement that needs review
• The accurate replacement, including plan, date, currency or market where relevant
• A link to your current canonical source
• Supporting documentation, such as a current product page or official release note
• A request to update the page or clearly mark the information as historical

Keep the tone factual and helpful. Do not ask a reviewer to change an opinion or remove a legitimate criticism. Correct objective details such as a discontinued price, an old feature matrix or an inaccurate product name. Where a page is an independent review, respect the publisher’s editorial control.

Check vendor portals for software review and product-listing sites your company manages. Update product descriptions, plans, integrations and screenshots through the official account. For affiliate partners, provide an updated copy block and clear terms, but do not assume they will use it. Set a reminder to follow up and record what changed.

If an organisation’s Wikipedia or Wikidata entry contains an incorrect fact, follow that platform’s rules and use its appropriate correction process. Do not treat community reference pages as company-controlled marketing channels. For claims that need independent confirmation, provide reliable secondary sources rather than asking editors to rely only on your own website.

Prioritise correction requests by impact. A high-visibility price comparison with a wrong number may deserve attention before a low-traffic forum comment about an old interface. You can use your source log to track the priority, contact date, response and next follow-up.

Tip: Give publishers one clear correction at a time. A short note with a specific URL and authoritative evidence is easier to act on than a broad request to “fix everything about our brand”.

Mistake to avoid: Treating third-party outreach as a guaranteed takedown or edit. Publishers control their own pages, and a correction request is not the same as an instruction.

Step 7: Report the answer and retest for regression

Use the feedback or reporting features available in the assistant where appropriate. They can help the platform receive information about a specific answer, but they are not a replacement for correcting the underlying sources. OpenAI’s help information about whether ChatGPT tells the truth explains that ChatGPT can produce incorrect answers. Perplexity explains how to report incorrect or inaccurate answers. Check the current interface because reporting options can change.

When you report an error, include the exact claim, a concise explanation of why it is wrong, and a link to the current authoritative page. Avoid submitting personal or confidential information. A report may help the platform review an answer, but do not promise that it will immediately edit a response or update model knowledge.

Retest the original prompt after you have made the source corrections. Use new sessions, keep the prompt consistent and record whether the answer is right, wrong or incomplete. Check the citations again. An answer can become accurate while still relying on a weak source, or cite a corrected page while omitting an important condition.

Do not use one successful answer as your finish line. Run a planned set of repeated tests over time and across relevant assistants. A five-run check can be a useful internal starting point, but it is not a universal standard or guarantee. Decide what is enough based on the risk and the importance of the fact. For a high-impact price or eligibility claim, use a more careful verification plan than you would for a low-impact descriptive detail.

Track at least these outcomes:

• Accuracy: Is the core fact correct?
• Completeness: Are important conditions, such as plan or market, included?
• Source quality: Does the answer rely on current, relevant pages?
• Consistency: Does the answer remain accurate across fresh sessions and wording variations?
• Coverage: Do the assistants and search experiences important to your audience show the corrected fact?

A change may take different amounts of time to appear across search tools and assistants. Crawling, indexing, source selection and model behaviour are not under your direct control. Avoid promising a fixed correction date to stakeholders. Report what you changed, what you have observed and what remains uncertain.

For a broader view of how brands can measure their presence across AI search experiences, explore SearchFIT’s resources. If you are comparing ways to manage this work, the SearchFIT comparison page explains one approach to AI search growth. Use measurement to guide the next investigation, not as a substitute for checking whether a specific answer is factually right.

Common mistakes to avoid

Changing the homepage before finding the source. A new homepage does not remove a stale PDF, review or old pricing announcement. Identify the source that is likely feeding the answer, then choose the correction that addresses it.

Publishing conflicting updates. If product marketing, sales and support use different plan names or price descriptions, an assistant may encounter the same inconsistency as a buyer. Confirm the approved facts internally before editing pages.

Making broad claims without conditions. “We support SSO” may be incomplete if it is available only on one plan or requires a configuration step. Say what is included, for whom and under what conditions.

Treating schema as a fix on its own. Structured data can help describe visible content, but it does not repair inaccurate pages or control what an assistant selects. Keep the page, feed and markup aligned.

Assuming every error is a hallucination. The answer may be repeating a real, publicly available but outdated statement. Find the source before deciding the model invented the claim.

Expecting a platform report to change the answer immediately. Reporting an answer is worthwhile, but it cannot replace the work of updating the sources you control.

Calling the problem solved after one test. AI answers can vary. Confirm the correction using a repeatable prompt set and check the citations as well as the wording.

A practical ongoing process

Treat fact accuracy as part of normal content and product operations, not a one-off AI search clean-up. Assign owners for pricing, product features, availability, security claims and policies. When a material fact changes, update the canonical page and the supporting sources at the same time. Add old, high-visibility pages to a review list instead of waiting for an assistant to expose them.

A lightweight monthly check can include a small set of high-value prompts, a review of cited sources, and a scan of recently changed product facts. Increase the frequency around a launch, price change, major policy update or seasonal catalogue change. Keep the same prompts where possible so you can distinguish a real shift from a change in how the question was asked.

If a wrong answer could affect a purchase, legal obligation or customer’s eligibility, involve the appropriate internal experts before making claims about the correction. In Moffatt v. Air Canada, the British Columbia Civil Resolution Tribunal addressed inaccurate information given by an airline’s customer-facing chatbot, as detailed in legal analysis of the Air Canada chatbot ruling and AI liability. You can read the tribunal decision on CanLII. It is a specific case, not a universal ruling about every AI answer, but it is a useful reminder that customer-facing information deserves careful review.

Wrap-up: fix the source, then verify the answer

When AI assistants repeat outdated prices or features, trace the claim before editing pages. Compare search-on and search-off answers, inspect citations and search results, correct your canonical facts, and ask third parties to update material errors. Then retest over time and be clear about what you can control: the accuracy and accessibility of your sources, not the exact timing or wording of every AI response.

The best next step is to review our 90-day roadmap to an AI-visible brand, choose one incorrect claim, document it, and assign an owner to follow it from source investigation through verification.