The quickest way to get less generic work from AI is to give it more useful detail when you ask a question, then save the background about your business so you do not have to explain it all again tomorrow.
Quick answer: Speak your prompts instead of squeezing them into a few typed words. Then ask an AI tool to interview you about your business, check the resulting notes for accuracy, and put those notes somewhere the tool can use them in future business chats. The first step helps with one request. The second helps across many requests.
AI can feel like Google on steroids when you first try it. You ask a strangely specific question and get an answer that seems to have read your mind. Then you ask it to write something for your business and out comes the slop: perfectly grammatical, completely forgettable, and not quite about you.
Usually the problem is that the tool has had to guess. There are two fairly simple ways to give it less guessing to do.
Why do short AI prompts get such generic answers?
Let’s say you type, “Find me a good Indian restaurant.” What does good mean? Where are you going? How many people are coming? What can you spend? The tool can give you an answer, but it has almost none of the details that would make that answer useful to you.
Now imagine saying this instead:
I’m going out with five friends in central Leeds. We’re on a bit of a budget and want somewhere cheap and cheerful that we can walk to, so we don’t need a cab. It’ll be about 10 pm. We might change our minds, so booking isn’t essential. What options should we check, and can you verify they’re actually open then?
That is not some whizz-bang prompting formula. You’ve just said what you actually need. The location, budget, group size, timing and walking distance all matter. An AI answer still needs checking—especially opening hours—but at least it has a proper brief.
What’s the easiest way to give AI a better brief?
Talk to it. Use voice typing on your phone, dictation on your computer, or the voice option in the AI tool you’re using. Unless you type at a ridiculous speed, speaking lets you get far more of the little details out of your head.
Don’t worry about sounding polished. Say what you want, who it’s for, what you’ve tried, what you don’t want, and anything that might change the answer. If you remember another useful detail halfway through, add it. Then send the prompt.
The point isn’t that more words magically make AI accurate. Relevant details give it a better chance of answering the question you meant to ask. The same goes for writing a customer email, planning a LinkedIn post or working through a business decision.
What is the difference between a detailed prompt and business context?
A prompt is the brief for the job in front of you. Context is the background that stays useful from one job to the next: what your business does, who your customers are, how you work, and how you like to communicate.
You can put some context into every prompt, but that gets boring quickly. If you use AI regularly, it makes more sense to keep the durable bits in a business knowledge file and add that file to a project or equivalent workspace in your chosen tool. My earlier piece on giving AI real context about your business explains why that matters. Here I want to concentrate on how to get that knowledge out of your head in the first place.
Account-level preferences can help with small things. I might tell an AI tool to call me Ade, keep its answers concise, or score a review out of 100 because I find that easier to understand. But those preferences aren’t the same as explaining my customers, services and working process.
How do you get all that business knowledge out of your head?
Let the AI interview you. This is much easier than sitting down in front of a blank document and trying to write “everything about my business”.
Start with a prompt like this:
Interview me about my business so we can make a useful knowledge file for future AI work. Ask one question at a time. Cover what I do, who I serve, what customers ask, how I deliver the work, and what happens afterwards. Keep moving across the whole business instead of drilling into one subject for ages. If something is unclear, ask me to explain it.
Then use voice mode or dictation and answer as you would if a sensible person were sitting opposite you. You don’t need to know the perfect structure before you start. The interviewer can ask the next question, and you can steer it when it goes off on a tangent.
How long should the interview go on?
Long enough to cover the width of your business, not three hours on one tiny corner of it. Half an hour is a useful starting point if you can spare it. Talk about your customers, the work you actually do, how enquiries arrive, what happens before delivery, what happens afterwards, and the questions people repeatedly ask.
If it starts getting forensic about something that doesn’t matter, say so. “That’s enough on pricing for now. Move on to who my ideal customers are.” You’re running the interview, not the other way round.
And if you run out of steam, stop. A decent first pass that you’ve checked is more useful than a huge document full of waffle.
How do you turn the conversation into a business knowledge file?
At the end, ask the tool to organise what you’ve said into a file you can read and check:
Turn this interview into a concise business knowledge file. Separate established facts from my preferences, ideas and things I’m unsure about. Don’t invent missing details. Put any unanswered questions at the end so I can check them.
You might get one file or a few smaller ones. Either is fine. Read them before treating them as a source of truth. Correct the mistakes, cut the filler and make sure a half-formed idea hasn’t somehow become “company policy”.
If the interview or the file includes real client enquiries, remove confidential names and details before uploading it to an AI project. You can usually explain the useful business pattern without giving away a customer’s private information.
I usually find the talking easier on a phone and the file handling easier on a computer. So you can do the interview on your phone, then open the conversation on a computer to download or copy the notes and add them to your AI project’s knowledge area. The exact buttons vary between tools; the useful part is the checked file, not the particular menu you clicked.
What can you do once the file is there?
Start a business chat within that project and give the AI a real job. Paste in a customer enquiry and ask for a reply that fits how you actually work. Ask it to help plan a LinkedIn post for a particular audience. Ask it to compare an idea against the customers and constraints in your file.
Tell it to say when the file doesn’t contain an answer. You still have to check the result, but it is much less likely to fill every gap with something that sounds plausible and isn’t true.
You can also give it a more grounded brief when you want to put business software you’ve barely used to work. It helps to explain what the business needs before asking what a clever tool could do.
None of this is about “training” a model in the technical sense. It’s about giving it a better brief now and reliable reference material for later. Speak more freely, keep the useful knowledge, and check what comes back. That’s when it starts to feel less like AI slop and more like actual help.
FAQ
Do I need to type a long prompt every time?
No. Dictation or voice chat can get the detail out much faster. Once you’ve saved the background about your business, your next prompt can concentrate on the specific job.
Does a longer prompt guarantee a better answer?
No. Relevant details help the tool understand what you mean, but you still need to check recommendations, facts and anything time-sensitive.
What if I don’t know enough about my business to make a knowledge file?
That’s exactly why an interview helps. Start with what you do know, answer one broad question at a time, and leave anything uncertain marked as a question rather than making it up.
Should the knowledge file contain every idea I’ve ever had?
No. Keep confirmed information, useful working preferences and current decisions clear. Put ideas and experiments in a separate section so the AI doesn’t mistake them for settled facts.
Will a project remember everything from every chat?
Don’t rely on that. Put the important information into the project’s files or instructions, check the result when you use it, and update the file when the business changes.