Why the Best AI Prompt Is Just a Well-Developed Point of View
Everyone is trying to learn how to write better prompts. There are prompt libraries, “proven frameworks,” LinkedIn threads full of examples with triple-digit engagement. The consensus is that prompting is a skill you acquire — one where you learn specific syntax, certain trigger phrases, the right way to ask for a table or a persona or a chain of thought.
That’s mostly wrong.
The biggest unlock I’ve had with AI tools isn’t a prompting technique. It’s having a point of view before I open the chat window.
What most prompts are missing
When someone says “the output felt generic,” they usually mean they gave a generic prompt. But they usually don’t blame themselves for the prompt — they blame the model. “It’s just not creative enough.” “It doesn’t really understand nuance.”
What the model is actually doing is averaging. It gives you the median of everything it knows about the thing you asked for. That’s not a bug; that’s the math. And the only way to pull the output away from the center — toward something specific, interesting, defensible — is to put specificity in.
Your point of view is the specificity.
Why “better prompts” is the wrong frame
The prompt isn’t the product. The idea is the product. The prompt is just how you load the idea into the tool.
If you want the AI to write an introduction to a blog post, and you don’t already know what the post is arguing, you’re going to get an introduction that argues nothing. The model will pad it, generalize it, and deliver something that reads like content. Not a piece of writing that has a perspective.
But if you walk in knowing exactly what you think — “this post is arguing that consistency in design isn’t about rules, it’s about intention, and most design systems fail because they document the rules but not the reasoning behind them” — then you have something to load. The prompt becomes a delivery mechanism for a thought that already exists.
That’s the whole thing.
The practical test
Before you open the chat, ask yourself: could I make this argument in conversation, without the AI? Could I defend it? Could I name one person who’d disagree and one person who’d immediately share it?
If you can’t, the AI can’t either. It’ll simulate the argument, but simulation at scale is still generic.
The people who get remarkable output aren’t prompt engineers. They’re opinionated people who’ve spent time thinking about specific things — and the AI is just the translation layer. They don’t need special syntax. They need something to say. The prompt is downstream of the thinking, not a substitute for it.
This is why the “prompting is a skill” framing subtly misleads you. It suggests the bottleneck is on the input side of the tool. But for most people, the bottleneck is on the thinking side — what do I actually believe about this, what’s the specific angle I want to take, what’s the version of this argument that couldn’t have come from anywhere else?
Answer those questions and the prompt almost writes itself.
What you put in is what you get out, scaled. If you want better output, the investment isn’t in the prompt. It’s in developing the opinion that makes the prompt worth writing.