You typed your brand name into ChatGPT or asked Perplexity what people think of your product, and the answer stung. Maybe it called you overpriced, said you're hard to work with, or recommended a competitor instead. If your search afterward looked something like "AI saying negative things about my brand," the instinct is to fix it right away, but jumping straight to a rebuttal usually makes things worse. This guide walks you through a process for negative or critical AI mentions: capture what the assistant actually said, work out whether the criticism is fair, and decide what to do about it. By the end you'll have a repeatable way to respond to bad AI brand answers instead of reacting to each one as it comes, and a process to fix negative AI mentions that holds up the next time it happens.
One thing to be honest about upfront. You can influence what AI assistants say about you by improving the evidence they draw from, but you cannot force ChatGPT, Gemini, or any other model to say something nicer. There's no button that reverses negative brand sentiment on ChatGPT overnight. What you can do is make the true, current, complete picture of your brand easier to find than the outdated or one-sided version, and do that consistently enough that it shows up in how these tools answer.
Capture the exact negative answer
Before you touch anything, write down exactly what happened. Run the original prompt again and record the full answer, not just the sentence that upset you. Note which assistant you used, the date, and whether it appears to have used web search or citations, since that changes how you trace it back later. Save every link, source, competitor name, or review site the answer mentions, along with the exact wording it used to describe your brand.
Then run a few close variants of the question, because a single prompt rarely tells the whole story:
- "What are the drawbacks of [your brand]?"
- "Who should avoid [your brand]?"
- "How does [your brand] compare with [a competitor]?"
- "What do customers complain about with [your brand]?"
- "Is [your brand] good for [a specific use case]?"
Don't reword the original prompt to try to get a nicer answer. The exact phrasing that produced the criticism is part of your evidence, and you'll want it later to prove whether anything actually changed.
You know this step is done when you have a dated record of the full answer, its sources, several prompt variants, and the exact negative language, detailed enough that a colleague could reproduce your investigation without relying on your memory of what happened.
Pro tip: save the answer before you start opening or editing the pages it points to. Source pages get updated or taken down, but the original answer is your baseline. If you lose it, you lose your ability to prove anything changed later.
The most common mistake here is reacting to one sentence without reading the paragraph around it. A negative-sounding line can be a fair limitation, a direct quote from a review, or a comparison that only applies to a specific buyer type. Each of those calls for a different response, so read the whole answer before you decide what kind of problem you have.
If you searched "negative brand sentiment ChatGPT" because this keeps happening rather than once, tracking a set of prompts already will get you here much faster next time. DeepSmith's AI Visibility tracks the questions that matter to your brand on a schedule and keeps the answer history, so instead of manually re-running a prompt when something looks off, you can pull up how the answer has changed over time. It separates mentions from citations too, which matters here: an assistant can name your brand without linking anywhere, or cite a page without describing you well.
Classify the criticism before you respond
Not every negative statement needs the same fix, and treating them all the same is how teams waste weeks. Most people who land here searching "AI saying negative things about my brand" are picturing one villain to fight, when what they usually have is a mix of the categories below. Sort what you found into a few buckets.
| What you're looking at | What it usually means | First move |
|---|---|---|
| Fair criticism | A real limitation, a recurring complaint, or a reasonable trade-off | Acknowledge it, give the context, say who the product isn't for |
| Missing context | The criticism is understandable but the answer leaves out something important | Publish a clear source that fills the gap |
| Weakly sourced criticism | A vague claim, an old opinion, or a low-quality source repeated as fact | Trace it, improve the evidence available, respond calmly |
| Factual error | Something demonstrably false or outdated | Treat separately as a correction, not a sentiment problem |
| Unfavorable comparison | You lose on price, support, features, or fit | Address that dimension honestly, trade-offs included |
| Mixed assessment | Strengths and real limitations sit side by side | Don't erase the limitation, make the strengths easier to verify |
Ask yourself four questions about each statement: is it an opinion or a verifiable fact, is it about your whole brand or just one plan or segment, does the source actually back up what the assistant said, and would a reasonable buyer actually find it useful. Those answers point you toward the right response.
This step is done when every negative statement on your list has a category, a note on how good the source is, an owner, and a proposed response. You've separated sentiment work from plain factual correction, which keeps the next steps from turning into a fight about the wrong thing.
Where teams go wrong is labeling every unfavorable opinion as misinformation. If real customers keep raising the same complaint, that's a signal for your product or support team before it's a content problem. And never publish a page that denies something you know is true. That reads as evasive to anyone who checks.
Trace the sources behind the answer
Once you know what kind of criticism you're dealing with, follow it back to where it came from. Open every citation the answer showed you and find the exact passage that seems to support the negative statement. Note the page, the publisher, when it was written or updated, and whether the assistant represented it fairly. Group similar sources together: several pages might all repeat the same complaint about response times or pricing complexity, which tells you it's a pattern rather than one bad review.
AI search doesn't always answer from a single page. Google has documented that AI Overviews and AI Mode can use query fan-out, running several related searches across subtopics before assembling an answer. ChatGPT's web-search answers can expose citations you can open and check yourself. That makes tracing possible when citations are shown, but a missing citation doesn't mean nothing influenced the answer. If you get an uncited answer, search the distinctive phrases and claims directly, and search the complaint as a concept rather than just your brand name, pairing your brand with terms like "support complaints" or "alternative" to see what turns up.
You'll know this step is done when you have a source map: the claim, the likely source, the exact passage, how current and credible it is, and whether other independent sources back it up too. It's also worth checking whether a competitor is winning the comparison simply because they have clearer, more plentiful evidence than you do.
Don't assume the first citation you see is the only cause of the answer, and don't confuse being cited with being described favorably. A citation count on its own doesn't tell you a page caused a particular response or that it reflects the citation signals that actually decide these things.
DeepSmith's Pages and competitor-citation views help here, showing which pages get cited for the prompts you track and which competitors are winning those citations. Combined with the Prompts area and its answer history, you can compare the same criticism across different prompt phrasings and different engines side by side.
Choose correction, counter-response, or new evidence
With the source traced, you have three real options, and picking the wrong one wastes effort.
Correction only makes sense when something you control is actually wrong or outdated: an old price on your own pricing page, a feature description that no longer matches the product, a policy that changed. Google's Refresh Outdated Content tool exists for pages that no longer exist or have changed substantially, and it's meant for cases where you don't own the page. It isn't a way to dispute an opinion, delete a public result you don't like, or force a recrawl of a page that hasn't actually changed. If the issue is on a page you own, update it transparently as part of your regular content refresh cycle and request normal recrawling through your webmaster tools. Don't just change the date to make an unchanged page look fresh.
A counter-response fits when the criticism is real but incomplete or framed unfairly. Acknowledge the specific point rather than denying everything, state the relevant context plainly, and point to evidence a reader can actually check. Say what's changed if you've made improvements, and name the trade-off honestly instead of talking around it. A structure that tends to work: that limitation is relevant for one kind of buyer, for a different situation the decision comes down to a specific factor, and here's the current evidence along with what changed. When the issue involves a specific customer's account, it's fine to acknowledge it publicly and move the details to a private follow-up, but the public part still needs enough context that it doesn't look like you're hiding something.
New evidence is the right call when the criticism reflects a real gap in what's out there: a transparent comparison page that lays out trade-offs honestly, a support guide that answers the actual implementation question, a case study that stays specific to its own customer rather than generalizing, or a changelog that shows what changed and when. What you shouldn't write is a generic "why we're the best" page. It needs to answer the specific criticism in a way that's useful even to someone who ends up choosing a competitor.
You'll know this step is done when your team can explain, for each piece of criticism, why you chose correction, counter-response, or new evidence, and the response addresses the actual claim rather than just asking an assistant to describe you more nicely. The costliest mistake here is publishing a wave of promotional content that repeats positive claims with no evidence behind them. That tends to make a brand look evasive rather than confident, and it doesn't close the information gap that caused the problem.
Publish evidence that answers the criticism
Whatever you build here should be built around the reader's decision, not around making an uncomfortable answer disappear.
A page that earns its place gives a direct answer near the top, states the limitation or criticism plainly instead of burying it, and explains the conditions under which it actually matters. It includes real evidence with dates and methodology where relevant, first-hand experience where it applies, and links to primary documentation rather than vague claims. It's honest about what's changed since older criticism was written, and where it makes sense, it says plainly who the product isn't a good fit for.
Google's guidance on helpful content is direct about this: original reporting, clear sourcing, demonstrated expertise, first-hand experience, and transparency about how something was produced all matter. It also warns against simply rewriting other sources or changing a date without a real update. Using AI to help produce content isn't against the rules on its own, but automation used mainly to game rankings is, and AI assistance doesn't earn any special ranking boost by itself. The output still has to be useful, accurate, and trustworthy on its own merits.
Before you publish, run through a short checklist. What exact criticism does this page address. What would a skeptical reader learn here that wasn't obvious from your existing pages. Is the evidence current and dated. Is there a visible, qualified author or reviewer behind it. Can a reader actually verify what you're claiming. Does the page admit the limitations that are genuinely there. And does it read like it was written for a buyer, not just for a crawler.
This step is done when a neutral reader can find the criticism, understand your position, check the evidence for themselves, and decide whether the product is right for them. The page should add real information, not repeat marketing language dressed up as an answer.
Avoid publishing ten thin pages that each target a slightly different version of the same criticism. One direct, well-evidenced page usually does more than a pile of shallow ones, and burying an important qualification under a long promotional intro undercuts the whole effort.
This is where DeepSmith's Content Studio earns a mention, since it can turn one of these evidence-backed gaps into a researched, internally and externally linked, publish-ready article with its own metadata and cover image, all grounded in the product details, claims boundaries, and voice stored in Deep IQ. That said, a person on your team who actually understands the issue still needs to sign off on sensitive claims and any customer evidence before it goes live.
Reinforce credible positive signals without faking them
Publishing one good page isn't the end of the work. Make sure the evidence is actually easy to find and easy to interpret everywhere buyers and AI systems already look.
That means linking the new page from your relevant product, documentation, support, and comparison pages, updating the pages that answer nearby questions so nothing contradicts what you just published, and making sure important pages are crawlable and not accidentally blocked from indexing. It also means routing recurring criticism to whoever owns the underlying issue, whether that's product, support, or leadership, because fixing the real problem does more for sentiment than any page ever will. Invite genuine customers to leave honest feedback through your normal review channels, and keep a single internal source of truth so your own pages don't end up disagreeing with each other.
What you should never do is buy, fabricate, or coordinate positive reviews, ask customers to claim things they don't believe, or seed undisclosed promotional comments in forums. That kind of manufactured signal tends to get noticed, and it's a bad trade for the credibility it costs. The legitimate version of this work is covered in our guide to earning genuine third-party mentions, and it takes longer precisely because it's real. Genuine, specific, verifiable positive evidence is what actually moves how AI describes you over time.
A simple loop to work from: identify a criticism that keeps recurring, decide whether the fix is product, support, policy, or content, publish the clearest evidence you can, link it from related pages so it isn't isolated, share it through the channels that make sense, and record the date so you know when to check back.
You'll know this is working when your relevant pages are consistent with each other, easy to find, and connected, and when you've actually addressed the underlying issue rather than just published a rebuttal to it. Don't turn this into a volume contest. More pages don't automatically mean better sentiment, and one candid, authoritative page often outperforms a dozen repetitive ones.
DeepSmith's Content Map can help you see where competitors have useful material on a topic and you don't, and Opportunity Agents can surface evidence-backed ideas, whether that's earning a citation for a tracked prompt, changing how AI describes a specific claim, or closing a gap a competitor currently owns. Content Studio can schedule the resulting pieces, and the Apps Library helps adapt a finished article for the channels where it needs to show up. None of that guarantees inclusion in a future answer or a change in sentiment. It supports the work; it doesn't do it for you.
Recheck the answer as a trend, not a one-off
Once the relevant source, product, or policy work is actually live, go back and rerun the original prompt along with your variants. Look at whether the negative statement still shows up, whether its wording has softened, whether the answer now cites your updated evidence, and whether the same criticism still appears across other assistants. A single improved answer isn't proof of anything by itself, since AI answers can vary from one run to the next even without anything changing on your end. This is the same trend-based discipline behind any AI visibility recovery effort, not a single lucky rerun. Independent research on how AI visibility rankings shift has found that much of the run-to-run change is statistical noise rather than a real change in how you're described.
Keep a response log with columns for the prompt, the engine, the date, the answer, the sentiment, the criticism theme, the cited source, what you did about it, when you published it, and what you observed afterward. That log is what turns a one-time fix into something you can actually track.
Worth knowing as you read your own data: Bing's AI Performance reporting describes its numbers as aggregated and sampled, refreshed daily with a short delay, and meant for spotting trends rather than accounting for every individual answer. A grounding query can map to several pages, and a page can show up under more than one grounding query, so citation share is a percentage of the citations shown for a given query, not a precise count of how often you were the answer. Google has said that pages appearing in AI features like AI Overviews and AI Mode get folded into your overall Search Console traffic, which makes regular search performance worth watching alongside how you show up in AI answers directly, though ordinary traffic on its own doesn't prove sentiment actually shifted.
This step is done when you have a before-and-after record built from repeated checks, not one screenshot, and you can say clearly which themes have improved, which criticism is still showing up, and what still needs product or customer-experience work rather than more content.
Don't promise yourself or anyone else a fixed reversal date. Don't call it a win because the brand showed up positively once. And don't assume that a change you see in one assistant means anything changed in the others. Keep your original answer on file, because it's still the baseline everything else gets compared against.
What to do next
Keep the loop running: capture what the assistant actually said, classify it honestly, trace it to a source, choose the right kind of response, publish evidence that holds up, reinforce it without faking anything, and recheck it as a pattern rather than a single result. That loop is what it actually takes to fix negative AI mentions instead of just reacting to the one that happened to catch your attention this week. Bring recurring criticism to your product and customer-experience teams too, since a lot of what shows up in AI answers is really a signal about the product, not just about your content. Treat this as a standing practice rather than a one-time fire drill, the same way you'd track brand reputation across every engine you watch. The honest version of how to improve AI brand sentiment is slower than a single rebuttal, but it's the version that actually holds up the next time someone asks.
If you want a way to keep this whole process from living in a spreadsheet you update by hand, DeepSmith tracks the prompts, citations, and competitors behind a mention, and helps turn the evidence-backed gaps it finds into content, all from one place. You can start a free trial and see how it fits into your own response process.
