There’s a moment every prompter experiences. You’ve described exactly what you want. You’ve been specific. You’ve given context. The AI responds with something that’s technically correct but completely misses the mark on style, format, or feel.
You stare at the screen, frustrated. “That’s not what I meant,” you think. But how do you explain what you meant when words keep failing you?
The answer is simple. Stop trying to describe it. Show it instead.
Examples are the most underutilized tool in prompting. When you show AI what good looks like, something powerful happens. It stops guessing about your preferences and starts matching them. The difference isn’t subtle. It’s transformative.
Human learning has always followed a simple pattern. We learn faster from models than from descriptions. Apprentices didn’t become master craftsmen by reading about technique. They watched, mimicked, and refined. Students don’t learn to write well by memorizing grammar rules. They read great writing and absorb patterns. AI works the same way. Show it excellence and it mirrors excellence back.
Why Descriptions Fail Where Examples Succeed
Think about trying to describe the color blue to someone who’s never seen it. You could use a thousand words and still not capture it. But show them the sky and they understand instantly.
That’s the gap between describing what you want and showing what you want. Descriptions require interpretation. Examples eliminate interpretation. They provide a concrete model the AI can analyze and replicate.
When you tell AI to “write in a casual, engaging tone,” what does that mean? Casual how? Engaging for whom? There are a thousand ways to interpret those words. But when you show AI three examples of the exact tone you want, it understands immediately. It can analyze patterns, match style, and deliver something that actually fits.
Research on AI training shows that including just two relevant examples improves output accuracy by 64% compared to text descriptions alone. That’s the difference between something you can use and something you have to completely rewrite.
Picture an Art Teacher (No Pun Intended)
Imagine you’re teaching someone to paint landscapes. You could spend an hour explaining perspective, color theory, brush techniques, and composition principles. They’d understand the concepts intellectually but struggle to actually paint.
Or you could show them three finished landscape paintings and say “create something like these.” They’d study the examples, notice patterns in color choices, see how shadows work, observe composition techniques, and produce something far closer to what you wanted. Same goal. Different approach. Completely different results.
Examples bypass the need for perfect explanations. They communicate through demonstration instead of description. AI learns from patterns in your examples the same way art students learn from studying masterworks.
What Makes a Good Example
Not all examples work equally well. The best examples share specific characteristics that make them useful teaching tools.
First, they’re relevant to what you’re asking for. If you want a social media post, show social media posts. If you want a formal business email, show formal business emails. Obvious, but people often grab whatever’s convenient instead of what’s actually similar to their need.
Second, they’re recent and current. Styles change. What worked five years ago might feel dated now. Use examples that reflect current standards and expectations for your format and audience.
Third, they demonstrate the specific qualities you care about. If tone matters most, choose examples with perfect tone. If structure matters most, choose examples with excellent structure. Your examples should highlight whatever aspect is most important for this particular task.
How Many Examples You Actually Need
Here’s where people get confused. They think more examples always equals better results. Not true.
For most tasks, two to three examples hit the sweet spot. One example might be a fluke. Two show a pattern. Three confirm the pattern and give AI enough data to understand your preferences without overwhelming it with information.
Think of it like this. If you’re teaching someone your coffee preference, you don’t need to show them fifty cups of coffee you’ve enjoyed. Show them three and they’ll spot the pattern. Medium roast, splash of milk, no sugar. Done. The same principle applies to examples in prompting.
There are exceptions. For highly specific or nuanced requests, you might need four or five examples. For simple, straightforward tasks, even one strong example can be enough. But most of the time, three is your target.
Seeing Examples Transform a Prompt
Let’s watch what happens when you add examples to a prompt.
Without examples, you might write this. “Create a catchy social media post announcing my new blog article about productivity. Make it engaging and conversational.” The AI will create something. But “catchy,” “engaging,” and “conversational” mean different things to different people. You’re rolling the dice on whether it matches your actual style.
Now add examples. “Create a catchy social media post announcing my new blog article about productivity, written in this style. Example one: ‘Everyone says they want to be more productive. Almost nobody wants to hear the truth about what that actually takes. Here’s the truth: [link].’ Example two: ‘You’re not lazy. You’re just optimizing for the wrong things. Let me explain what I mean: [link].’ Now write one for my article about morning routines.”
See the difference? The second prompt shows AI exactly what “catchy” and “conversational” mean in your voice. It can match the pattern, the rhythm, the tone, and the structure. The output will sound like you because you showed it what “you” sounds like.
When Examples Matter Most
Examples aren’t always necessary. For simple, straightforward requests where the format is standard, you can skip them. “Summarize this article in three sentences” doesn’t need examples. The format is clear and universal.
But examples become critical when you’re asking for something with subjective qualities. Tone, style, voice, creativity, personality. These elements are hard to describe but easy to demonstrate. Anytime you find yourself using words like “casual,” “professional,” “engaging,” “friendly,” “authoritative,” or “creative,” stop and ask yourself if an example would communicate better than the description.
Examples also shine when you’re creating something that needs to match existing work. If you’ve written three blog posts and want a fourth in the same style, show AI the first three. It’ll match your voice far better than any description could achieve.
The Compound Effect Nobody Mentions
Here’s what happens when you start using examples consistently. Your prompts get shorter. You stop struggling to find the perfect descriptive words because you’re showing instead of telling. You save time. You get better results faster.
But there’s a deeper benefit. Using examples forces you to curate and understand what excellence looks like in your field. You start noticing what makes good writing good, what makes effective marketing effective, what makes clear explanations clear. That awareness makes you better at your work, whether you’re using AI or not.
Examples aren’t just a prompting technique. They’re a learning tool. Every time you choose examples, you’re clarifying your own standards. That clarity compounds into everything you create.
The best communicators throughout history understood something fundamental. Showing beats telling. Every time. Demonstrations convince where descriptions fail. Models teach where explanations confuse. This hasn’t changed in thousands of years. AI just made it more obvious because the results appear in seconds instead of weeks.
Stop describing what you want in vague terms. Start showing AI exactly what good looks like. The transformation in your results will speak for itself.