How to Check Whether AI Copy Preserves Your Meaning

“Some plans include exports” leaves your reader with a question: does mine? A polished paragraph can leave that question unanswered while sounding perfectly clear.

Discourse and pragmatic processing help explain the gap between readable sentences and useful answers. Two papers from 2024 offer a narrow look at those abilities. They’re useful for designing checks on your AI drafts. They aren’t a verdict on the model writing your next product page.

What are discourse and pragmatic processing in AI?

Discourse processing means following meaning across sentences or turns in a conversation. Pragmatic processing means interpreting what a speaker implies, using the surrounding context and the question being answered.

For a hypothetical SaaS page, consider these two sentences: “You can export your reports. Exports are available on the Pro plan.” Following the discourse means connecting the second sentence to the promise in the first. Reading the implication means asking whether someone on another plan would expect access.

The distinction matters because a sentence can be grammatically correct without answering the reader’s actual question. “Some plans include exports” says something about plans. It doesn’t identify the plan a particular reader needs.

Cho and Kim’s 2024 study examines a narrower example: whether models interpret “some” as implying “not all.” The researchers also tested how a question supplied as context affected that interpretation.

That kind of inference is called a scalar implicature. The technical name describes a familiar move: a reader hears a weaker statement and infers that a stronger statement doesn’t apply. Someone says “some,” and the listener takes them to mean “not all.”

You don’t need to use that term in your writing brief. You do need to separate three checks:

• Does each sentence make sense on its own?
• Do the sentences preserve the same facts and limits together?
• What would a reader reasonably take the whole answer to mean?

Those checks ask different questions. A clean sentence answers only the first. For your published copy, the last two deserve their own review rather than getting folded into a general request to “make it clearer.”

What did the BERT and GPT-2 study actually find?

BERT and GPT-2 both treated “some” as implying “not all” without context in the first experiment reported by Cho and Kim. The second experiment found a difference between the models when the researchers supplied a question as context.

The paper calls that contextual cue the “Question Under Discussion.” In plain language, it’s the question the statement is supposed to answer. The study reports that BERT performed consistently across the question types tested. GPT-2 had difficulty with a question type that required inferring the implication in context.

That finding is narrower than “AI can’t understand meaning.” It also doesn’t show that AI handles context reliably. Both sweeping claims would reach beyond the reported experiments.

The researchers used two sets of experiments, drawing on cosine similarity and next sentence or token prediction. Those methods examine model responses through specific tests. They aren’t a review of finished SaaS pages or a trial of your current writing workflow.

Keep these parts of the result separate:

• What worked: both models showed the “not all” reading of “some” without context.
• What caused difficulty: GPT-2 struggled with a particular contextual question type requiring that inference.
• What remains untested here: whether your current model preserves the intended meaning in your product copy.

The useful finding is that performance depended on the task and context. You can borrow that distinction when checking drafts without pretending you’ve repeated the research.

A model’s handling of one word in a controlled test and its handling of your full pricing page are different questions. The paper gives you evidence about the former. You still need examples from your own product to examine the latter.

These papers don’t establish how your current writer performs

The supplied studies don’t test the current model and prompts you use to write product copy. Cho and Kim’s study names BERT and GPT-2, while Robrecht and colleagues’ paper argues that pragmatic abilities need more systematic study.

The second paper describes research on these abilities as less extensive and less systematic than research on grammar-related abilities. That supports asking for better tests. It doesn’t establish a failure rate for today’s writing tools.

The publication year doesn’t fill that gap. A paper published in 2024 about BERT and GPT-2 remains evidence about those models under the reported conditions. It doesn’t become a test of another model because both belong to the broad category of language models.

Use the papers to choose better checks, not to declare your current writer competent or broken.

For a solo founder, that’s a useful limit. You don’t need to settle whether AI “really understands” language before reviewing a draft. You need to know whether a specific draft preserves your product’s facts and answers your reader’s question.

Start by separating three kinds of statement:

• A research result: what the paper reports about the models it tested.
• A local observation: what your current model produced for a saved prompt.
• A recommendation: what you think someone should check before publishing.

Label them honestly if you write about your results. “This prompt dropped our plan restriction” is a useful observation. “Modern AI can’t understand context” would require evidence these sources don’t provide.

That restraint doesn’t weaken the point. It gives readers something they can inspect, repeat, and disagree with on the facts, rather than asking them to accept a sweeping claim.

Which reader question should you put in the prompt?

Put the buyer’s exact question in your writing brief, alongside the facts needed to answer it. Cho and Kim’s study makes the question being answered an explicit part of its second experiment, rather than treating a sentence as context-free.

That doesn’t prove a particular prompting technique works. It does suggest a sensible question for your own review: did you tell the writer what the reader needs to find out?

For a hypothetical reporting product, compare these briefs.

The vague brief says: “Write a confident paragraph about exports.”

The more testable brief says: “The reader is on the Basic plan and wants to know whether they can export reports. Only Pro includes exports. Answer their question directly. Don’t imply Basic includes the feature.”

The second brief gives you a clear standard for checking the result. You can see whether the answer identifies the restriction. With the first brief, “confident” says little about which facts must survive.

A compact writing brief can contain:

1. The reader’s question, written as they would ask it.
2. The product facts that answer it.
3. The limits that must appear beside the promise.
4. The false impression the draft must avoid.

Keep the brief short enough that you’ll actually use it. A single clear question is more useful for this check than a page of adjectives about your brand voice.

Then read the draft against that question before editing its style. If the reader asks whether their plan includes exports, a paragraph praising flexible reporting hasn’t answered them.

You can still ask for crisp, persuasive copy. Just don’t let those instructions replace the answer you owe the reader. Treat the exact question as part of the writing task, not background you assume the model will infer.

Test whether caveats change the promise a reader hears

A caveat can narrow a promise without removing the broader impression created by the opening sentence. Treat that as something to check in your draft, not as a proven failure of every AI writer.

The distinction follows the problem explored by Cho and Kim: interpreting words alone differs from interpreting them against a question. For product copy, your practical test is whether the full answer supports the decision the reader needs to make.

Use hypothetical cases like these, replacing the details with your actual product facts:

• A caveat after a headline. “Export every report” appears above “Available on Pro.” Ask what a Basic customer would think they can do.
• An ambiguous setup promise. “Ready in minutes” appears without saying whether it describes creating an account or finishing the required setup. Ask which step the promise covers.
• A feature limited to one plan. A feature list says “Includes exports,” but the page discusses several plans. Ask which buyers the claim applies to.

These are suggested checks, not results reported in either paper. Their purpose is to expose the difference between including a fact somewhere and making the answer clear.

For each example, write the acceptable reading before you ask for a rewrite. Otherwise, you risk judging the new draft by whether you prefer its wording.

You can also ask the model to explain what a reader might infer. Treat that answer as another output to inspect, not independent proof that the original copy is safe.

Your final check should compare the promise with the actual product rule. If exports require Pro, both the headline and the supporting sentence should leave that restriction clear. Don’t approve the draft merely because a correct caveat appears somewhere on the page.

How should you review an AI draft for meaning?

Review an AI draft against a written reader question and a short set of product facts before polishing its tone. The paper by Robrecht and colleagues calls for more systematic study of pragmatic abilities; your editorial check should be narrower than a claim to measure those abilities in general.

You’re checking whether this draft is fit to publish. You aren’t building a full language benchmark.

Use a repeatable pass with four steps:

1. Write the expected answer. State what the reader should know after reading. For a plan question, name the eligible plan.
2. Mark each promise. Highlight claims about access, setup, price, or outcomes. Match each claim to a product fact you can verify.
3. Read for implied promises. Ask whether a reasonable reader could take the wording further than the facts allow.
4. Rewrite and check again. Inspect the new draft against the same question and facts, rather than approving it because it sounds better.

Keep factual accuracy and implied meaning separate during the review. A sentence can name the correct feature while leaving its availability unclear. Your notes should identify which problem you found.

For example, “Exports are available” may be true for your hypothetical product. If the reader asks whether Basic includes exports, the sentence still leaves the decision unresolved. A useful correction names the plan instead of adding more praise for the feature.

Save the prompt, the draft, and the edit when you catch a problem. Those records give you concrete examples for the next brief. They also make it easier to check whether a revised prompt fixes the same issue.

Don’t give the model a permanent pass after one good answer. Approve the copy you’ve checked. That’s a much smaller claim, and it’s the one your publishing decision actually needs.

Publish examples that readers can inspect

A useful account of your AI writing test includes the model version, prompts, and examples. Without those details, a reader can’t tell what you tested or compare your result with their own workflow.

Cho and Kim’s paper identifies its models, methods, and contextual conditions. You don’t need to imitate an academic paper, but you should preserve the same basic boundary between a finding and a broader claim.

If you publish a short post about a draft that went wrong, include:

• The model name or version shown by your tool, and the test date.
• The relevant prompt, including the reader question and product facts.
• The output that created the problem, quoted accurately.
• Your explanation of the misleading reading and the correction you made.

If your tool doesn’t expose a version, say so. If you changed the prompt between attempts, show the change. If you only tested one example, call it one example rather than a pattern.

A narrow result can still make a useful article. You could show how an export restriction disappeared during a rewrite, then explain the edit that restored it. That gives another founder something concrete to check without claiming all models make the same mistake.

Keep the research claim equally narrow. The 2024 study found particular results for BERT and GPT-2. Your saved draft shows what happened in your own test. Neither automatically proves what will happen on the next prompt.

Before your next page goes live, write the reader’s question at the top of the draft. Read the headline, answer, and caveat against it. If a buyer could leave with the wrong expectation, rewrite the promise before you spend another minute improving the rhythm.