Why your AI answers are generic (it's the prompt, not the tool)
Four things separate a usable AI answer from bland filler, and most prompts skip at least two. A worked example, the data warnings that matter in regulated sectors, and 84 free prompts written for real business tasks.
The most common complaint we hear about AI tools is that the answers are generic. Bland, obvious, faintly corporate, and not really usable without a rewrite.
Almost always, the tool is fine. The prompt was the problem.
"Write me an email to a client about a price increase" will get you something that could have been written for any business in any industry, because that is precisely what you asked for. Nothing in the request tells the model who it is, who you are, what happened, or what a good answer looks like.
The four things a good prompt has
A prompt that produces something usable does four jobs, and most bad prompts skip at least two.
A role. Tell it what it is. "You are an experienced B2B salesperson preparing for a first discovery call" produces different output from no instruction at all, because it narrows what the model draws on.
Context. The specifics. The company, the situation, what has already happened, what you know and what you do not. This is the part people skimp on, and it is the part that does the most work. A prompt with three lines of real context beats a clever one with none.
An output format. Say what you want back. Five bullets. A table with these columns. Under 150 words. Without it you get whatever length and shape the model felt like, which is usually longer and vaguer than you wanted.
A constraint. The part almost everyone leaves out, and the one that separates a usable answer from filler. "Do not invent facts about the company. If something is not in the text above, say unknown rather than guessing." Or "no exclamation marks, no corporate euphemism". Or "if the honest answer is that we are the wrong fit, say so."
That last category matters more than it sounds. Language models are built to be helpful and will fill gaps confidently rather than admit they do not know. Telling them explicitly not to is the single most effective thing you can add.
A worked example
Here is the bad version, which is what most people type:
Write a follow-up email after my sales meeting.
And here is a version with the four elements in it:
Write a follow-up email after a sales meeting.
My notes: [rough notes] What we agreed: [next steps] Their main concern: [concern]
Rules: under 150 words. No pleasantries beyond one short opening line. Restate their problem in their words before mentioning anything we do. End with one specific question that is easy to answer. British English, plain, no sales jargon, no exclamation marks.
The second one takes thirty seconds longer to write and produces something you can send after a light edit rather than a rewrite. Save it once and you never write it again.
Free library, 84 prompts
Rather than explain the theory and leave you to it, we have written the prompts.
Eighty-four prompts across seven areas: sales, HR, finance, marketing, legal, construction, and healthcare and care. Every one follows the structure above, with placeholders in square brackets so it is obvious what to replace. Search across all of them or browse by category, expand one and copy it.
They are written for actual business tasks rather than demonstrations. Preparing for a discovery call. Turning a call transcript into a CRM record. Drafting a variation on a construction job. Structuring a supervision session in a care setting. Querying a supplier invoice without starting a fight. Getting a testimonial that says something specific instead of "great service, highly recommend".
Nothing to sign up for, and nothing sent to us.
The bit that matters more than the prompts
Four of the categories carry a warning before you use them, and they are worth reading.
The biggest risk with these tools is not a poor answer. It is confidential information leaving your business. Anything typed into a consumer AI tool may be retained and used to train future models. That is a commercial problem in most sectors and a regulatory one in some.
If you work in care or healthcare, entering resident or patient identifiable data into a public AI tool is very likely a UK GDPR breach and potentially reportable to the ICO. If you work in legal, do not paste privileged material or unredacted client contracts, and check every case citation, because models fabricate them convincingly. In finance, do not ask a language model to do arithmetic; use it to structure and explain, and do the maths in your spreadsheet. In construction, anything safety-related must be reviewed and approved by a competent person before it is used.
The practical answer to most of this is to use a business-grade tool covered by your own tenant's data protection terms, rather than a free consumer account. If you are on Microsoft 365 Business Premium you may already have one, which is worth checking before you buy anything.
Where to go from here
If prompts are the immediate win, the bigger one is automating processes rather than tasks. Our AI Use Case Finder gives you a ranked shortlist for your sector and size, and the AI Readiness Assessment covers the groundwork that decides whether any of it sticks.
If you would like a hand getting your team confident with this, that is a large part of what we do. Two hours on real work changes more than any policy document. Get in touch and we will talk it through.
Written by CT1 Technologies
