AI content creation tools: what they do well and where they fail

Most teams meet AI writing through a chat window. Someone types “write a product description for a beige cotton curtain” and gets a tidy paragraph back in seconds. The paragraph is fluent. It is also generic, and it may state a width nobody supplied.

This article explains what AI content creation tools are good at, where they go wrong and how to set them up so the drafts are worth editing.

Three kinds of AI content creation tools

The label covers three different things.

• General chat assistants. You type a request and get text. They are flexible, and everything depends on what you remember to tell them.
• Template-based writing apps. They wrap a model in forms: pick “product description”, fill in the fields, choose a tone. They are quicker for routine work and limited to the forms the vendor designed.
• Custom generators. A tool built for one business and connected to its catalogue, style rules and publishing systems. It takes longer to set up, and it knows your data without being told.

What they do well

All three sit on the same sort of language model. A language model produces text by predicting which words are likely to come next, based on patterns in the text it was trained on. It does not look facts up unless the tool around it supplies them. So it is at its best when the facts already exist and the job is to reshape them.

• First drafts of routine text: descriptions, announcements, replies to common questions.
• Rewriting: shorter, longer, more formal, simpler.
• Variants: one announcement shaped for an email, a social post and a web banner.
• Translation and adaptation between widely used languages.
• Structure: turning rough notes into an outline, or a long document into a summary.

Where they fail

Invented detail is the main risk. Ask for a description without giving the specification and the model fills the gaps with plausible numbers. A reader cannot tell which figures were supplied and which were guessed. Neither can you, unless you check.

The second problem is sameness. Left to its defaults, a model writes in a smooth, even voice that sounds like everyone else using the same tool. Stock phrases creep in. A customer who reads ten product pages in that voice stops reading.

The third is missing knowledge. A model does not know your new price, last week’s policy change or the complaint that arrived this morning. It will write about them anyway if asked.

The brief decides the output

Most disappointing output traces back to a thin request. A useful brief answers five questions.

• Who is reading, and what do they already know?
• What are the facts? Paste them in. Do not expect the model to know them.
• What should the reader do afterwards?
• How long, and in what tone? A sample of your own writing helps more than an adjective.
• What must not appear? List the claims you cannot support and the words your brand avoids.

How to compare tools

Compare “write a post about our new opening hours” with a request that gives the hours, the branch, the reason for the change and two earlier posts as a style sample. The second takes a little longer to write and needs far less editing. Use requests of that second kind when you test.

Feature lists look alike, so test with your own material. Take five real tasks from last week and run them through each candidate. Then ask the following.

• Does it accept your data, such as a product file or a style guide, or only typed text?
• Can you choose the model, or is one fixed?
• Where is your text processed and stored, and is it used for training? Read the terms.
• Can several people share templates and review each other’s drafts?
• Does it connect to where the text ends up, such as your shop or your email system?

Editing is the step nobody can skip

Treat every output as a draft from a quick junior colleague who has never seen your product. Check each fact against the source. Cut the sentences that say nothing. Put back the detail only you know: the customer question that prompted the piece, the thing that went wrong last year.

Search engines look at the result as well. Google’s guidance on generative AI says that producing many pages with such tools without adding value for users may violate its spam policy on scaled content abuse. The same guidance asks site owners to focus on accuracy, quality and relevance. Volume on its own works against you.

Reading the text aloud is the cheapest test there is. A sentence you would never say to a customer across a counter should not be on your site either.

Chat window or custom tool

A chat assistant and a careful brief are enough for a handful of texts a week. The effort sits in the briefing and the editing, and no purchase removes it.

Once the volume grows and each text has to match your catalogue and your voice, an AI content generator connected to your own data starts to justify the setup. That second kind is what we build at Webitro.