Misclassification has several causes
An inaccurate answer can look like one problem while containing several. The assistant may confuse your organisation with another name, infer a category from vague wording or repeat an old description that remains common online.
Website copy can also make the wrong interpretation reasonable. If the page lists many activities without explaining the primary customer problem, a model may group the organisation with whichever category has the strongest language signals.
Technical access creates another possible failure. A page may load in a browser while exposing little readable text to a crawler. In one measured run, 321KB of HTML contained only 4.2 percent readable text, which illustrates how much apparent page content may be unavailable as usable description.
No single diagnosis should be assumed from one incorrect answer. The same question can receive different responses on different runs, so the error must be observed across conditions.
Start with the exact wrong idea
Save the answer that contains the misdescription. Copy the relevant sentence, the cited sources and the question that produced it. Record whether the assistant searched the web and whether you supplied the organisation’s name.
Your next task is to classify the error, because the four common kinds call for different responses:
- the name attached to the wrong entity altogether
- the organisation placed in the wrong category
- the offer described with a claim that was true once
- the category broadly right while the audience, location or use case is wrong
Which one it turns out to be determines what is worth changing.
| Kind of error | How it shows up | What is worth changing |
|---|---|---|
| Wrong entity | Attributes belong to a different organisation with a similar name | Identity signals repeated across every public surface you control |
| Wrong category | Named competitors come from an unrelated market | Direct language about the problem served, on the pages models actually read |
| Outdated claim | A description that was true at some earlier point | Several public sources corrected together, not one page |
| Wrong audience or use case | Category is broadly right, the buyer described is not | Explicit statements of who it is for and where it applies |
Compare a named question with a need-based question. Where the assistant only gets it right once the name appears in the prompt, the public information is probably retrievable without being connected to the wider need.
One measured run produced 41 citations in total, every one of them on the four questions that named the organisation outright. The need-based questions produced no citations. That pattern shows why a named answer can create false confidence about open discovery.
Make the organisation legible
Rewrite important pages so a reader can identify the organisation’s central offer quickly. State who the organisation serves, what problem it addresses and what kind of work it does in language that matches customer questions.
Use one consistent description across the home page, service pages, directory entries and other authoritative profiles. Keep the wording accurate rather than repeating a category merely because it seems commercially attractive.
Explain the organisation’s boundaries as well. A clear statement about what the organisation does not provide can reduce an interpretation based on a neighbouring category, although no wording can force every assistant to respect that distinction.
Check the page in a text-only view and inspect whether the key statements are present without requiring client-side interactions. Machine-readable markup can support interpretation, but it cannot compensate for vague or contradictory visible copy.
The aim is not to write for a model at the expense of people. A concise, consistent explanation helps human visitors and gives retrieval systems fewer competing interpretations.
Test the copy change as an intervention
A copy change is worth treating as an intervention rather than an improvement, which mostly means capturing what the answers looked like beforehand. Baselines cannot be reconstructed afterwards and the temptation to skip one is strongest exactly when a change feels obviously correct.
What you grade matters less than grading the same things consistently before and after and keeping the raw text alongside whatever score you assign. A number can hide a sentence that is materially wrong.
After the change, the page needs time to become available to retrieval before the comparison means anything and the questions have to stay identical. One answer cannot establish a stable change, however encouraging it looks.
Look for specific movement in the results rather than a general impression:
- whether the category becomes more accurate
- whether the explanation reaches the intended audience
- whether suggested alternatives become more relevant
- whether named and need-based questions move in the same direction
The evidence can support a cautious statement such as, "After the copy change, accurate category descriptions appeared more often in these tests". It cannot support a promise that all assistants now understand the organisation.
Competitor lists can expose the frame
The competitors an assistant names will often reveal an inaccurate category before anything else does. A comparison list is not a neutral market map, because it reflects the category and use case inferred from the question and the available descriptions.
A company’s own site copy has been observed propagating into which competitors models named. The list changed completely after the copy changed.
That observation does not prove that website wording controls competitor recommendations. It does show that the organisation’s description can participate in the frame used to select alternatives.
Use this as a diagnostic rather than a target. Should the list change after a clearer description, inspect whether the new alternatives actually serve the same customer need. A different list is not automatically a better list.
What remains outside your control
You cannot directly edit a frontier model’s weights or dictate which sources it trusts. Other websites may continue to publish conflicting descriptions and some assistants may rely on stored information after your page has changed.
A universal correction cannot be inferred from one interface either. Model families, retrieval settings and prompt wording can produce different results, so a correct answer in a single run deserves confidence only in proportion to how often it repeats.
The next practical step is to save a baseline, identify the exact misclassification and revise the clearest public sources together. Then repeat named and need-based prompts, grade the specific error and publish the result with its conditions.
What remains genuinely uncertain is whether the corrected description will persist in answers that do not search the web. Improving the evidence available to an assistant is within reach and so is measuring what changed. Guaranteeing when or whether, every model adopts the same understanding is not.