Every conversation you have with another human comes preloaded with invisible bookmarks. You’ve got shared references, past interactions, facial expressions, and tone. When you say, “You know what I mean,” a real person probably does.
But ChatGPT doesn’t know your life, your job, or your mood unless you tell it directly. It’s not following a thread. It’s seeing a single snapshot with each prompt, and that snapshot might be missing half the frame.
So when you say something like, “Make this punchier,” but haven’t included what “this” is, or what you mean by “punchy”—funnier? shorter? more urgent?—you’re setting the model up to guess. Its guesses will be conservative, generic, and often way off the mark.
This isn’t the model’s fault. It reflects how our brains want to leap ahead, especially when we assume more of our own context is obvious than it actually is.
It’s Not the Model. It’s the Prompt
There’s a tendency to assume that because ChatGPT sounds fluent, it must be intelligent in the way we are. But LLMs don’t think. They predict. And they can only predict well when given the right setup.
It’s like asking a GPS, “Where should I go next?” without entering a destination, current location, or mode of travel. You might get a suggestion, but it’s not going to be useful.
The real issue isn’t how smart the model is. It’s that your prompt might be missing the bones of a good question: the who, what, and why. When you provide too little structure, the model fills in the blanks with the safest possible generalities.
If you’ve ever gotten a reply that felt eerily neutral or full of platitudes, that’s why. The model isn’t wrong. It just wasn’t guided clearly enough to be right.
The Lazy Prompt Loop
Once you’ve used ChatGPT a few dozen times, there’s a natural temptation to cut corners. You stop writing complete thoughts. You forget that the model can’t recall your earlier tone or goals unless you repeat them. You start typing the way you text a friend in a hurry. The problem with this is that you end up getting results that sound like you’re talking to a confused stranger.
This creates a cycle. You write less clearly. The model gets more generic. You assume it’s broken or “not as good as it used to be.” So you prompt even more vaguely, just to test it. And the spiral continues.
This loop is common, not because users are careless, but because the interface tricks us. ChatGPT feels like a conversation. But it is more like a command line in disguise. If you don’t specify what you want with intent, the machine defaults to what’s safest. And what’s safest is often what’s dullest.
The Confidence Bias
Egocentric bias is one of those annoying features of the human mind that works great in person and terribly online. We assume our ideas are clearer than they actually are, especially when we’re the ones expressing them.
You know what you meant when you wrote “summarize this,” but if “this” was part of a long chain of thoughts, or if your concept of a summary is “just the emotional highlights,” the model won’t know that unless you say so.
The tricky part is we tend to blame the output rather than our input. We say the model was off, or it didn’t get the tone right. But it’s not about intelligence. It’s about shared understanding. And the only shared understanding ChatGPT has is what you put in the box.
Prompt Engineering Sounds Silly. It’s Not.
The term “prompt engineering” still sounds like marketing lingo. But what it really describes is the practice of being precise. It means giving your instructions structure. It means laying out boundaries. It means telling the model not just what you want, but how you want it and why it matters.
In other words, it’s communication, just with a machine that won’t nod or ask for clarification.
If you’ve ever said to a colleague, “Can you take a pass at this, but keep it under 300 words and make sure it doesn’t sound too salesy?”—you were already doing prompt engineering. Now you just need to do it with the AI, too.
And no, you don’t have to sound robotic. You can still be natural, friendly, and conversational. But behind the tone, your instructions need to carry intent and clarity. That’s the trick. That’s what separates vague results from useful ones.
The Vague Input = Vague Output Law
There’s a principle at work every time you use a language model: the quality of your input sets the ceiling for your output. The model doesn’t have instincts. It has probabilities. And vague prompts produce vague averages.
This is why ChatGPT might give you a 10-point list when you wanted a short paragraph. Or a bullet-pointed summary when you wanted a persuasive pitch. If you didn’t say otherwise, the model did the default thing.
Precision doesn’t mean length. Sometimes a one-line prompt like, “Write a warm but professional thank-you email for a job interview with a nonprofit,” is more effective than a rambling paragraph about how you felt the conversation went. Because it gives the model a mission.
Want better answers? Be a better asker.
How to Check Yourself
Before you blame the tool, run a self-check. Think of this as your prompt hygiene routine:
- What exactly do I want? Can you summarize your ask in one clear sentence?
- What assumptions am I making? Is the model supposed to already know something I haven’t actually said?
- Would a stranger reading this understand the context? If not, add the missing pieces.
- Did I tell it how to format the output? Bullet points, essay, casual voice—if you care, say so.
- Did I mention constraints? Word count, tone, target audience—these shape the result more than you think.
This isn’t about writing longer prompts. It’s about writing smarter ones.
Yes, It’s Extra Work. But So Is Every Tool
People expect AI to be easy. And it can be, but only once you’ve figured out how to use it right. Like any tool, it demands a little friction upfront in exchange for flow later.
Think of the first time you learned to use Excel formulas. Or wrote your first SQL query. Or even just set up email filters. At first, it feels fiddly and annoying. Then it clicks. And you never go back.
ChatGPT is the same way. The difference is that the friction is in how you think, not how you code. It’s in the clarity of your ask. And like all clarity, it requires slowing down first.
One Last Thought
This isn’t about blaming yourself for “bad” prompts. It’s about recognizing what your brain is doing automatically and then learning to override it.
Because once you spot the gap between what you meant and what you actually typed, you’re already halfway to closing it. And once you start refining your inputs, you’ll find that the model gets sharper, faster, and more aligned to what you actually need.
You’ll stop wondering why it missed the mark.
You’ll start recognizing how close it came, given what you gave it.
And that’s the shift. Not in the tool. In how you use it.