Over the last couple years the buzz phrase ‘prompt engineering’ has morphed into a widely accepted term for using language models, suggesting a specialized skill that you might need to pay someone to learn, like juggling fire or cooking soufflés. At first, I filed it away into the “continuing education” department of my brain like I would with a new programming language or framework. Finally I took a closer look at what people were calling “prompt engineering” and my first thought was “what am I missing, isn’t this just writing?”
The more I see it used, the less I think we really need another fancy term for what is essentially clear thinking and effective communication. As we hurtle into a future where these real core skills are becoming an endangered species – and the rift between the superpower-havers and the have-nots threatens to get bigger rather than smaller – the more I become convinced that it does more harm than good.
In The Short Term
Granted, the various formalized techniques out there for structuring prompts can be super useful as a cheat sheet. This is especially true if you’re not already in the habit of thinking through problems systematically. But if you inspect these strategies more closely, you’ll notice that they follow a few common patterns. They all focus on clearly articulating what you’re trying to achieve, providing relevant context, and facilitating their thought process to make sure they’re considering the most important details. Sound familiar? That’s because these are fundamental skills we use in any form of communication, whether its with humans or machines.
One counterargument is that there are definitely some model-specific quirks and technical limitations that are invisible to a user lacking specialized knowledge. Things like proactively working around a model’s token limits and context windows can lead to better and more consistent results. Understanding certain special parameters like temperature is useful if you have any control over it. And sure, when you’re building production systems that need to squeeze optimal performance out of these models, that specialized knowledge becomes much more relevant.
In The Long Term
But here’s the thing. As these models become more sophisticated, a lot of these limitations are becoming less relevant, even to the power users and enthusiast crowd. Modern models (or rather, ensembles of models a.k.a. ‘agents’) are increasingly good at understanding natural communication and intent. The “engineering” part of prompting is gradually being absorbed into the models themselves. And with the most recent ‘reasoning’ models (like GPT-o1), trying to micro-manage or over-engineer them can actually make them worse.
Even in cases where we’re working with simpler, more specialized models, we’re likely heading toward a future where more advanced models orchestrate these interactions for us. In other words, the technical details of prompt creation get abstracted away, letting us focus on clearly communicating our goals rather than mastering specialized prompting techniques.
Why does it matter?
I worry that by mystifying these skills with fancy terminology, we’re creating real barriers for people who could benefit from this technology. Some engineers I know have been hesitant to try using AI tools because they don’t have time to learn a whole new set of skills. This is exactly the problem – we’re taking fundamental skills people already have and rebranding them in a way that makes them feel inaccessible.
In software development, we’ve already seen how intimidating terminology can shape behavior. For a minute everyone thought they needed a “DevOps Engineer” to use continuous deployment. “Data Science” still gets branded as a mysterious discipline, artificially separated from analysts and engineers who do similar work. The same pattern is emerging with AI interaction. People who are already excellent communicators and problem solvers are holding back either because they think they need specialized training first, or because they’ve been alienated by the over use of buzzwords.
The irony is that the best results often come from clear, straightforward communication rather than technically complex prompts. I’ve watched ‘non-technical’ people get impressive results from these tools simply by explaining their needs clearly and iterating on the responses – the same skills they use when working with human teammates. By treating AI interaction as some specialized discipline, we risk overlooking the value of these fundamental communication skills that everyone already possesses.
So what do we call it instead?
Maybe instead of searching for the perfect catchphrase, we should think about who we’re trying to reach. Different groups naturally gravitate toward different metaphors and frameworks that make sense to them.
For the sci-fi crowd (my people), something like “Robopsychology” might actually be perfect, if a little on the wacky side. It captures the essence of understanding how these artificial entities process information, and helps emphasize the squishier parts vs the purely technical aspects of communicating with them.
The creative community might connect better with concepts like “AI Collaboration.” This framing acknowledges the partnership aspect of working with thinking machines, rather than treating them as just another system to be engineered (or something that autonomously replaces the artist and should be avoided). Writing this blog has been a fun experience in this area.
For educators, terms like “Learning Design” already exist to embed these tools into the context of existing teaching methodologies and strategies. In this context, prompting is a skill that blooms naturally from forward-thinking educators like Lilach and Ethan Mollick.
The point here isn’t to create more buzzwords – we have enough of those already. Instead, it’s about finding ways to make these concepts more approachable and relevant to different communities. Just as good teachers adapt their language to their students’ understanding, we should be flexible in how we talk about AI interaction based on who we’re talking to. And knowing your audience is yet another skill which will never become obsolete.