AI writing tool: what it can do well, and where it gets things wrong

The easiest way to picture an AI writing tool is as a widely read assistant with an excellent memory for phrasing, but no access to today's news. It is trained on a huge amount of text and learns to guess which word most likely comes next — that is how a phrase becomes a paragraph, and a paragraph becomes a full answer.

What matters most: an AI text generator does not check facts and does not "know" truth the way a person does. It picks a plausible continuation — whatever most often followed a similar context in what it was trained on. When the plausible answer and the correct answer line up, the text comes out accurate. When they don't, you get a confident-sounding mistake. That is the one idea worth carrying out of this section: the model predicts, it does not verify.

Every strength and weakness covered below follows from that. Where the task is to lay out a familiar kind of thought in a familiar shape, prediction works very well. Where the task needs an exact memory of one specific fact, date, or narrow detail, prediction is a game of odds, not a guarantee.

An AI writing assistant is strongest on repetitive, well-defined tasks — the ones where what matters is coherent phrasing along a familiar pattern, not a unique memorised fact. Below are ten tasks where a text generator genuinely saves time, with an honest caveat attached to each one.

SEO content is a case of its own: it has its own keyword and structure logic that doesn't fit into one table row. How an AI writing tool handles that kind of text, and what to watch for, is covered in AI-generated SEO content. The same goes for expanding a list of points: where that kind of tool helps and where it fails is covered in how an AI text expander works.

• Facts, figures and quotes. The model can confidently state the wrong date, number or source for a quote — it picks a plausible answer instead of checking a reference, and from the outside a confident wrong answer looks identical to a correct one. What to do: verify every number, date or quote separately, the same way you'd check someone else's draft, before you publish it.
• Recent events. The model knows the world as of its training data and doesn't follow the news in real time, even when it phrases an answer as if it were up to date. What to do: for anything current, use a fresh source rather than asking the model to "remember" yesterday.
• Narrow expertise. In medicine, law, finance and other specialised fields, the text can sound confident and assured while being wrong in exactly the details that matter to a specialist. What to do: expert content always gets checked by someone with the relevant background, not just a copy editor.
• Links and sources. If asked for one, the model can invent a link, a book or a study that does not exist, simply because it looks plausible next to the rest of the text. What to do: open and verify every link the model suggests yourself — never paste one in by name alone.
• Long pieces. Over a long stretch, the model loses the thread and starts repeating itself — the same point shows up twice in different words, and earlier conditions from the brief quietly get dropped. What to do: request long material in sections and assemble it yourself, checking the transitions between pieces.
• Humour and a distinctive voice. Subtle humour, wordplay and a recognisable authorial voice come out weaker than neutral business writing — a joke can land flat and a style can go generic. What to do: rework those specific spots yourself, treating the draft as raw material rather than a finished result.
• Understanding your context without a brief. Left with too little to go on, the model fills the gaps itself — inventing details about your company, product or audience that you never gave it. What to do: the more detail in the brief, the less gets invented; a thin brief almost always produces a text that's about nothing in particular.

The full process, from idea to finished text, is covered in a separate guide: how to write an article with AI. Here it is in short — what actually improves the result across almost any task, once you know how to work with an AI writing tool deliberately rather than just typing a topic into the box and hitting the button.

• Write a detailed brief. The more specific the inputs — the topic, who it's for, what must be mentioned — the less the model has to invent on its own. How to put one together: how to write a prompt for text generation.
• Ask for the piece in sections, not all at once. A long piece is better assembled from parts: an outline first, then each section as its own request. That leaves much less room for the model to lose the thread partway through.
• Check it, rather than take it on trust. Verify facts, figures, dates and any specific claims — the same way you would check a colleague's draft.
• Edit it, rather than just copy it. What comes back is a draft, not a finished piece: strip the generic phrases, add whatever mattered to you but wasn't in the brief. For making the result sound less generic, see how to make AI text sound human.

These four steps apply to every task in the table above. The easiest way to see it for yourself is to open the text generator and try a two- or three-sentence brief on something of your own.

"Which is the best AI writing tool" is a natural question, but it has no universal answer: tools keep changing, and everyone's tasks are different — something that works great for customer emails can fall apart on a legal document. It is more useful to compare criteria than brands.

• Quality in the language you need. The same model can write noticeably better in one language than another — test it on your own text, not on someone else's example.
• Settings that fit the task. The ability to set a tone, ask for something shorter or longer, and work keywords straight into the brief — without that, you end up rewriting the result by hand every single time.
• Limits and pricing. How many free runs you get and what happens after that — worth knowing upfront, not after the limit runs out mid-task.
• History and saved results. If you come back to a piece a day or two later, it helps when the service keeps a history of requests instead of just the last result.
• Privacy and the terms covering generated results. Before publishing AI-generated text or using it commercially, check the specific service's own terms — this is a question for that document, not a general rule.
• A matching set of adjacent tools. Rewriting, shortening, translation and grammar checking in one place save time — no shuffling text between different services for every single operation.

There is a ready tool for this: iBro's text generator collects the brief and the settings on a single screen, with no multi-step wizard. The brief field takes up to 6,000 characters — keywords and other inputs belong right inside that same brief, since there is no separate field for them.

There are six settings: tone (friendly, expert, or a touch of irony among seven options), writing style (business, conversational, sales-driven and others — also seven), a request to make the text shorter or longer instead of a precise word count, a format for a specific platform — a Telegram post or an Instagram caption — a formatting toggle (paragraphs, lists, bold for a few key phrases), and an emoji toggle for when a few emoji fit the text.

The result is one call and one complete answer, with no intermediate steps. Signed-in users get their results saved to a 30-day history; guests get none. Without signing up, three free runs a day are available across all AI tools combined — enough to see whether this way of working suits you before you register.

Open the text generator and try it on your own brief — the difference shows up in the very first answer.

Expect a fast, coherent draft from an AI writing tool, not a finished final text: facts, narrow expertise and a distinctive voice still need a person to check and refine them. Try the text generator on your own task and decide for yourself where it saves time — and where it still needs your edit.