The model isn't lazy. It's guessing — because most of us hand it almost nothing to work with.
Last month I asked ChatGPT to write a follow-up email to a client who'd gone quiet after a proposal. What came back was polite, grammatically flawless, and completely wrong for the situation — it read like it could've been sent to anyone, about anything. I ended up rewriting most of it by hand, then sat there wondering what exactly I'd outsourced.
That's when it clicked: the problem wasn't the model. It was that I'd given it almost nothing to work with.
The model isn't lazy — it's just guessing
Take a request like:
Write a LinkedIn post about AI.
There's nothing here to anchor the answer. Who's it for? What angle? Educational, provocative, promotional? The model has to invent all of that itself — and when a model has to invent things, it reaches for the safest, most averaged-out version it can find. That's where the vague bullet points and hollow conclusions come from.
Now compare it to this:
Write a LinkedIn post for B2B SaaS founders about using AI to cut repetitive customer-support work. They already use AI, so skip the basics. One concrete example, not a list of use cases. Conversational tone, under 180 words. Open with a problem support teams actually recognize. No "AI is transforming the way we work" lines.
Nothing clever happened here. I just stopped leaving decisions on the table for the model to make on my own behalf.
Every vague prompt is a list of unmade decisions
"Write an email to a customer" could mean a dozen different things. Refund request? Sales follow-up? A support ticket that's been sitting untouched for three days? Each of those needs a completely different email — and the model has no way to know which one you mean.
One sentence changes everything:
Write an email to a customer whose support request has been unanswered for three days. Apologize for the delay, explain the issue is being looked into, and be clear about what happens next.
Now it knows what it's actually solving for.
Five gaps worth closing before you hit enter
You don't need a prompt engineering manual. You need to check five things — and they aren't a rival to the three-step method, they're that method in detail. Outcome, context and output are the three you can never skip. Audience and constraints are the two that start to matter the moment the task gets fussy.
The situation — your context. What's actually going on? What already happened that the model needs to know about?
The goal — your outcome. Not "write a customer email" — but what the email needs to do: calm someone down, get a missing piece of info, rebuild confidence.
The reader — your audience. A CFO evaluating a purchase and a first-year student learning the same concept need the facts delivered in completely different ways.
The constraints. Word count, no jargon, three options instead of a wall of text — these stop the model from drifting into filler.
The shape of the output. "Analyze these reviews" is an open field. "Give me the top five complaints, the three most requested fixes, and a table showing frequency" is a destination.
Before and after, on a real task
Here's what vague looks like:
Create a content strategy for my business.
And here's what happens when you close the gaps:
You're helping a B2B SaaS company that helps non-technical professionals write better AI prompts. Users already use ChatGPT but keep getting generic results. Build a 30-day content plan to drive organic traffic and get readers to try the product. Focus on practical problems, skip AI news and beginner explainers. Return a table: topic, audience, search intent, title, CTA — grouped into four content pillars.
Same request. Same amount of typing. Dramatically less guessing for the model to do.
The real skill isn't prompt engineering
It's noticing which decisions you've quietly handed to the model without meaning to. Next time an answer comes back flat, don't start rewriting it. Go back up and ask: did I explain the situation? The goal? Who it's for? Any real constraints? What the finished thing should look like?
Usually fixing just one of those is enough.