One of the major problems people run into with AI is that they begin with a vague notion of where they want to go, but they have not yet made the shape of the project concrete.
This is not only an AI problem. It is how product and engineering work all the time. A product manager describes a direction. An engineer comes back and asks: “Did you mean this, or did you mean that?” Much of the genius of senior engineers is knowing which implementation fits the actual context.
Everyday work has the same problem. You may have a general direction, but the milestones are fuzzy, the sub-decisions are unclear, and the execution path is not high-definition enough to act on easily.
People are vibe-coding their way through life.
This gets worse when the project crosses multiple domains. A solopreneur building a landing page may need copywriting, UX, visual design, positioning, conversion strategy, analytics, and implementation. They may know enough to want the outcome, but not enough to describe the work at the right resolution.
In my research around AI slop and AI craft, one thing keeps showing up: the limiting skill is often not model capability. It is user vocabulary.
The model can execute complex work to a surprisingly high standard. But it does not know what to do when the instruction is “make me a landing page.” It needs the user to know the relevant dimensions of the work: spacing, typographic hierarchy, offer structure, proof, objections, information architecture, visual rhythm, and the difference between a section that looks nice and a section that does a job.
When the user has richer vocabulary, output quality rises.
This is true whether you are prompting a machine or another human being. The more precisely you can describe what you want, the better output you will get from employees, collaborators, agencies, spouses, children, and AI systems.
What makes a good prompt is not magic syntax. It is the domain knowledge of the prompter.
This also explains why senior leaders sometimes struggle to get good AI output. The higher up the hierarchy you go, the more you operate at abstraction. Executives see how the parts fit together, but they may not have the fine-grained vocabulary for every layer of implementation.
That is not a flaw. It is the nature of abstraction.
The key is knowing which layer you are operating on. A CEO should not prompt like a designer. A designer should not prompt like a data engineer. A product lead should not prompt like a founder writing a manifesto.
Good AI work depends on matching the instruction to the right level of abstraction.
If you want better output, get more domain knowledge. Understand the layer you are operating within. Then prompt with fine-grained detail appropriate to that layer.
The model is powerful.
But vocabulary is what points it.