Let me take you on a quick journey through the history of human consciousness and communication. Why is this important you might ask? To understand prompt engineering, we need to see it for what it truly is. It isn’t just a technical trick or a productivity hack; it’s part of a much deeper leap in how we transmit and produce output. Prompt engineering is the foundation of this, and it lets us engage with the machine. Technically, it’s a rhetoric for the AI age.
In other words, learning how to prompt is learning how to speak with and through the accumulated memory of humanity itself. I’ll explain why below:
(oral → written → print → digital → AI)
Orators were the first “interfaces” or “LLM” between memory and meaning. They didn’t just recite; they performed, and knowledge was alive.
Then came the leap into inscription: clay, papyrus, parchment. For the first time, memory could exist outside the human body. Consciousness itself began to externalize into symbols.
With Gutenberg, knowledge scaled. The printing press democratized literacy, accelerated science, and remade religion. Consciousness shifted again: books became our teachers, and our minds became trained in sequential thinking.
Fast forward to the digital age: knowledge became hyperlinked, decentralized, and instantaneous. We no longer absorb information in straight lines but hop across networks. Consciousness adapted once again to a web-like, rhizomatic.
Now we are at a new threshold. Why? Because now, knowledge is once again dialogic. It speaks back. It makes mistakes, refines and adapts. It now assimilates images, photographs, videos, and even code. We are no longer bound to a single mode of expression either. At this point, with a written or oral prompt, we can summon a picture, render a diagram, generate music, code an app. Output itself has become multifaceted and conversational.
However, as with many emerging technologies, there’s resistance.
Acknowledging resistance to the prompt
In Plato's Phaedrus, he argued that writing weakens memory, because true knowledge should be embodied, lived, and spoken. He saw writing as shadows compared to the living dialogue of philosophy.
When the printing press emerged, it was met with fierce resistance from scribes, monks, and manuscript producers who saw it as a threat to their social and economic standing.
In 2008, Nicholas Carr famously asked in The Atlantic: “Is Google Making Us Stupid?” He argued that the internet was rewiring our brains, eroding our ability for deep reading and sustained concentration, replacing it with skimming and distraction.
Ted Chiang in a New Yorker essay observes: “Using ChatGPT to complete assignments is like bringing a forklift into the weight room; you will never improve your cognitive fitness that way.” The piece warns that AI may lead to an age of “infinite regression to the mean,” where manuscripts (and even consciousness) become homogenized rather than deepened.
Overall, every era’s communication leap has its utopians and its critics, both arguing not just about utility, but about the reshaping of consciousness. So this begs the question: How do we stay on top as “humans” but elevate output? What is the next creative/strategic step? The answer is to elevate our taste with a prompt. So, where does GPT-5 fit into this?
Why prompting matters more now with GPT-5
As models advance, they begin to contain more parameters, think of these as decision-points or reference markers the model can use when generating an answer. While this increase makes the model more powerful, it also increases the complexity of choosing which parameters to draw from in order to provide the most appropriate response.
One way to think about this is through a map analogy:
Imagine you’re navigating from Point A to Point B. The destination represents your desired outcome (the answer you want from the model).
With fewer parameters (like in older GPT models), the map is simpler, maybe just highways and major roads. Getting from A to B is straightforward, but limited.
With more parameters (Like GPT-5), the map has multiple layers: not only highways, but also small roads, walking paths, train routes, bike lanes, and even scenic detours. Each new parameter adds another layer of choice, just like adding details such as elevation, traffic conditions, or scenic value.
This added detail/parameters means that prompt engineering matters more than ever. If you just type in a vague prompt, it’s like asking for “directions” without specifying whether you want the fastest route, the most scenic route, or the cheapest route. But if you engineer your prompt carefully, you guide the model to filter through the right parameters and deliver an output that best matches your needs.
It’s also worth noting that sometimes, simpler models with fewer parameters can actually be more efficient and outperform larger ones in narrow tasks, just like sometimes taking a direct bus is faster than juggling multiple transfers across trains and trams. Still, today’s advanced models require (and reward) deeper prompt engineering for general-purpose reasoning.
GPT-5 doesn’t just generate output; it invites participation. It reminds us that intelligence has always been dialogic, a conversation between what we know, what we ask, and what emerges in between.
In this new epoch, prompting becomes the new literacy. Those who master it will not only get better answers but also learn to ask better questions, reshaping consciousness.