Everyone's using AI. Not everyone's getting the same results from it.
The difference, most of the time, is the prompt.
A prompt is simply the instruction you give to an AI. It's what you type (or what gets sent behind the scenes in a built system). The AI reads it, processes it, and responds based on what it understood you to be asking for.
That last part is where things get interesting. What you meant and what the AI understood aren't always the same thing.
Think of It Like Briefing Someone New
Imagine you've just hired someone who is extraordinarily capable but knows nothing about you, your business, or what you're trying to accomplish. They're smart. They'll do exactly what you ask. But they have no context, no history, and no ability to read between the lines.
If you walk up to that person and say "write me an email," you're going to get an email. Probably a generic one. Addressed to nobody. About nothing in particular. Because that's all you told them.
If you say "write a follow-up email to a client named Sarah who we met with last Tuesday about her HR onboarding process. Keep it short, friendly but professional, reference that we talked about the document automation piece, and ask if she has questions before we send a proposal" — now that person has something to work with.
That second version is a better prompt. Not because it used special words or techniques. Because it gave enough context to produce something actually useful.
What Makes a Prompt Work
There are a few things that separate a prompt that gets you somewhere from one that gets you a generic non-answer.
**Context.** Who is this for? What's the situation? What does the reader already know? AI has no access to any of this unless you provide it. The more relevant background you include, the more relevant the output.
**A clear goal.** What do you actually want at the end? A draft email? A bulleted summary? A yes or no answer with reasoning? "Help me with my marketing" is a goal. It's also too vague to be useful. "Write three subject line options for an email promoting our spring maintenance package to existing clients" is a goal the AI can actually hit.
**Format instructions.** If you want a list, say list. If you want it short, say short. If you want it to sound casual, say casual. AI defaults to whatever format feels reasonable for the request — which may not be what you had in mind.
**Constraints.** What should it avoid? What tone is wrong for this situation? What's already been tried? Constraints help as much as directions. "Don't use the word solutions. Don't open with I. Keep it under 100 words." Those are constraints that shape the output significantly.
The Google Search Problem
A lot of people treat AI like a search engine. Short query. Keywords. See what comes back.
That works fine for search because search is looking for existing content that matches your terms. AI is generating a response based on what you asked. The difference matters.
"Email template" typed into Google finds you email templates. "Email template" typed into Claude gives you a very generic email template, because that's all you asked for.
The mindset shift is from searching to briefing. You're not looking something up. You're giving instructions to something that will build what you describe. The more clearly you describe it, the closer the result is to what you actually needed.
System Prompts vs Your Prompts
When you use a built AI tool (a chatbot on a website, an AI assistant embedded in a business system, a custom workflow we've built for a client) there are usually two layers of prompting happening.
The system prompt is the set of instructions that was built in before you ever showed up. It tells the AI what its job is, how it should behave, what it should never do, what tone to use, and what it has access to. You don't see it. It runs in the background.
Your prompt is what you type in the moment.
Both matter. A well-written system prompt means the AI already knows it's a customer service assistant for a flooring company, should only answer questions about flooring and scheduling, and should always offer to connect the user to a human for complaints. Your prompt then just has to be the actual question.
When we build AI tools for clients, a lot of the work is in the system prompt. That's where behavior gets defined. Your prompts, and your clients' prompts, layer on top of it.
You Don't Have to Be Technical to Do This Well
Prompt writing is a skill, but it's not a technical one. It's a communication skill. You're learning to give clearer instructions to something that takes instructions literally.
If you've ever written a good job posting, briefed a contractor, or explained a process to a new employee, you already understand the core of it. The same instincts apply. Be specific. Provide context. Define what done looks like.
The people who struggle with AI most are often the ones who treat it like a magic box. Put vague question in, expect precise answer out. The people who get consistently good results treat each prompt like a brief. They put thought into it because the quality of the output depends on the quality of the input.
A Quick Before and After
Vague prompt:
"Write something for my newsletter."
What you'll get: a generic newsletter intro about nothing specific to your business.
Better prompt:
"Write a 150-word intro for my monthly newsletter to small business owners. The main story this month is about automating their invoicing process. Tone should be conversational and direct, not corporate. Don't open with 'I' or 'We.' End with a one-sentence hook into the main article."
What you'll get: something you can actually use, or at worst edit for 90 seconds and send.
The difference isn't magic. It's specificity.
The Bottom Line
A prompt is an instruction. A good prompt is a complete brief. The gap between the two is the gap between AI that feels disappointing and AI that actually saves you time.
You don't need to learn a new skill set. You need to get more deliberate about telling AI what you actually need. The same way you'd explain it to a smart person who just walked in the door.
Michelle Onizuka is co-founder and Systems Architect at Onizuka Studio. She builds AI systems for small and mid-size businesses and spends a lot of time thinking about why some prompts work and others don't.
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