How to Get ChatGPT to Describe Your Brand Correctly

You can't edit what ChatGPT says about your brand directly — there's no submission form and no support queue for it. The description is assembled from training data and live retrieval, so the only durable fix is to change what the web says. Audit the description, trace each wrong fact to the page causing it, and correct it there.

That's slower than a button and faster than it sounds. Most wrong brand descriptions trace back to a handful of pages, and several of them are usually yours.

Where the description actually comes from

Two mechanisms, and treating them as one is why most attempts to fix this fail.

Parametric memory. What the model absorbed during training. It's frozen at training time, carries no citations, and you cannot influence it on any useful timescale. If ChatGPT confidently describes a product you sunset two years ago, this is usually why.

Retrieval. When the model searches the live web mid-answer, pulls a few pages, and writes from those. Sources get cited. This is the surface you can move, and it's the one that matters commercially, because "what does [company] do" is exactly the kind of specific, current question that triggers a search.

The practical consequence: an answer with citations is fixable this quarter; an answer without them is fixable whenever the model is next trained. Check which one you're looking at before you spend anything.

Step 1: Audit what it says now

Run these prompts in a fresh, logged-out chat. A personalised session with memory on will feed your own framing back to you and you'll conclude everything is fine.

Prompt	What it tests
`What is [brand]?`	The baseline description
`Who is [brand] for, and what does it cost?`	Whether pricing and audience facts are current
`Is [brand] legitimate?`	What trust signals the web is supplying
`What are alternatives to [brand]?`	Whether you're placed in the right category
`Who founded [brand] and when?`	The entity-level facts most likely to be stale

Run the same five in Claude, Gemini and Perplexity. Four models reading the same web give you quick triangulation: a fact that's wrong in one place is probably a one-off confabulation, while a fact that's wrong in all four is coming from sources — and sources you can find.

Log the answers verbatim with the date. You need the before-state to know whether anything you do works.

Step 2: Classify the error before you fix it

Not all wrong descriptions have the same cause, and the repairs differ.

Stale fact. The model has an old price, an old positioning, a former product name. Cause: the current fact isn't stated clearly and recently anywhere prominent, so retrieval keeps landing on older pages.
Wrong category. You're described as a directory when you're an exchange, or as an agency when you're software. Cause: the web describes you in the language of an adjacent category — usually because you do.
Entity confusion. The description is about a different company with a similar name. That's a disambiguation failure, which is the core problem entity SEO exists to solve.
Hedging. The model says it doesn't have reliable information. Cause: too few independent sources. This is the most common result for a young brand and, annoyingly, the healthiest starting point — nothing has to be un-learned.
Genuine confabulation. A plausible-sounding fact that exists nowhere. There's no source to fix; it usually stops recurring once the retrievable material gets denser.

Step 3: Trace the wrong fact to its source

When an answer carries citations, open every one. When it doesn't, reverse-engineer the likely source by searching the wrong claim itself in quotes, plus your brand name. You'll normally find the culprit inside ten minutes, and it's usually one of four things:

An old page of yours you forgot about — a two-year-old pricing page, a stale press release, an outdated About section. The single most common cause, and the easiest possible fix.
A directory or aggregator listing you submitted once and never updated. These rank well for brand queries and get retrieved constantly.
A review-site profile with old plan names or a wrong category tag.
One widely-copied article whose error has been syndicated across a dozen low-effort sites.

Step 4: Fix it at the source, in priority order

Your own site first. Free, immediate, under your control. State the current facts in plain sentences a model can lift without surrounding context: what the product is, who it's for, what it costs, when it launched, who runs it. Put them where they're unambiguous — About page, homepage, a real pricing page with actual numbers rather than a "contact us" wall. Add Organization schema with a sameAs array pointing at every official profile you own; schema markup is the closest thing to a machine-readable statement of identity you get to make about yourself.

Then delete or update the old pages. Correcting the new page while the wrong one stays live and indexed just gives retrieval two answers and lets it choose.

Then the listings you control. Directory entries, review-platform profiles, social bios, conference speaker pages, podcast show notes. Write the description once and paste it verbatim everywhere. Exact repetition across independent domains reads as corroboration in a way that ten paraphrases doesn't.

Then third-party coverage. This is where the real leverage is, and where it stops being free. Models weight independent sources far more heavily than your claims about yourself, which is why brand mentions carry weight even without a link. Roundups, comparison posts, community threads and guest content describing you correctly are what eventually outvote a stale fact.

Then, if one specific article is the problem, ask for a correction. A polite, specific email naming the wrong sentence and supplying the right one gets a surprisingly high hit rate from real publishers. It gets nothing from content farms — don't spend the afternoon.

What doesn't work

Telling the model to remember a correction. It applies to your session, not to anyone else's.
Posting the correct description on your own blog fifty times. One domain is one source no matter how loudly it repeats itself.
Buying "AI reputation management." Most of what's sold under that name is the source-correction work above, marked up. Some of it is mass-generated pages, which is scaled content abuse and makes the problem worse.
Blocking the crawlers, then complaining. If OpenAI's search crawler can't reach your site, your corrected page can't be retrieved and the model falls back on whatever third parties say. Blocking the training crawler while allowing the retrieval one is a defensible stance; blocking both isn't compatible with wanting a better description.

How long it takes

Retrieval-driven answers can shift within days of the underlying pages changing — you're not waiting on a model update, you're waiting on a crawl. Answers coming from training memory don't move until the next training cycle, which you don't control and can't schedule.

So expect to fix the citable, current-facts layer quickly, and expect the stale-memory layer to fade slowly as corrected material accumulates. Re-run your five prompts monthly and keep the log.

Frequently asked questions

Can I contact OpenAI to correct what ChatGPT says about my company? There's no product feature for editing brand descriptions and no queue to join. OpenAI runs a privacy request process aimed at personal data, not company positioning. For business facts, the working route is changing the sources the model retrieves from.

Why does ChatGPT have outdated information about my business? Either the answer came from training data frozen before your change, or retrieval found an older page that still states the old fact — often one of your own. Search the outdated claim in quotes and see what comes back.

Does ChatGPT read my website directly? Only when it searches. OpenAI documents separate bots for retrieval-time fetching, user-triggered browsing and training, and you can allow some and block others — see what an AI crawler is.

How do I know whether an answer came from retrieval or training? Look for citations. A cited answer was retrieved and reflects current pages, so source edits will move it. An uncited answer came from memory, and it won't change in the short term no matter what you publish.

Should I add an llms.txt file to fix this? It won't hurt, but don't expect much — the major AI systems fetch these files rarely. A clear, current About page and a public pricing page do far more, because those are the pages retrieval actually lands on.

What if ChatGPT confuses my brand with another company of the same name? That's a disambiguation problem, not a content problem. Consistent naming, sameAs schema, and third-party sources that pair your brand name with your category are the fix — the same work behind getting a Google knowledge panel.

The bottom line

A language model describing your brand is summarising the web's consensus about you, and it does that whether or not the consensus is accurate. The fix is unglamorous and the order matters: correct your own pages and retire the stale ones, make every profile you control say the same sentence, then get independent sites to describe you correctly often enough that the old version is outvoted.

That last part is the slow half, and it's mostly a matter of being referenced by relevant sites in your field. Backlinkster covers one piece of it — a 1-for-1 exchange where site owners in related niches trade in-content links, each placement verified live by code, so the mentions building your description are real and stay up. Five verified swaps a month on the free plan; the plans are here.