Hey everyone, welcome back to My Weird Prompts! I am Corn, and I am joined as always by my brother.
Herman Poppleberry, at your service. It is great to be back in the studio, Corn. Our housemate Daniel sent us a really provocative prompt this week that has been rattling around in my head since breakfast.
Yeah, Daniel is always good for a reality check. He wants to know if prompt engineering is just a temporary phase. You know, a time-limited first wave of artificial intelligence that is already losing its edge. He is asking what the long-term skill set actually looks like for mastering these tools as we move into twenty twenty-six and beyond.
It is a great question because the landscape has shifted so much in just the last twelve months. Remember twenty twenty-three? People were getting paid three hundred thousand dollars a year just to write clever sentences. Now, here we are in January of twenty twenty-six, and the models are so much more intuitive. It raises the question: was prompt engineering ever really engineering, or was it just us learning how to speak a new language that was still a bit glitchy?
That is the hook, right? If the goal of AI is to understand human intent, then a perfect AI should not need engineering. It should just understand you. But I suspect it is not that simple. I want to dive into this idea of context engineering versus prompt engineering. But first, Herman, give us the lay of the land. Where are we right now with how these models process our instructions compared to, say, two years ago?
Well, the biggest shift is in what we call instruction-following capabilities. Back in the day, you had to use these very specific hacks. You had to say things like, let us think step by step, or you had to threaten to give the model a tip, or tell it that your career depended on the answer. It was almost like psychological manipulation of a statistical model. But today, the frontier models have internalized those reasoning paths. They have been trained on so much high-quality chain-of-thought data that they do the reasoning automatically.
So, those little tricks are becoming redundant. I have noticed that if I try to use an old twenty twenty-four style prompt with a current twenty twenty-six model, it sometimes actually performs worse because I am over-complicating the instructions. It is like trying to give hyper-detailed driving directions to someone who already has a high-resolution map and GPS. You are just getting in the way.
Exactly! That is a perfect analogy. We have moved from the era of hacking the prompt to the era of defining the objective. The models are much better at figuring out the how if we are clear about the what. But that does not mean the skill is gone. It has just evolved. I think what Daniel is getting at is that the low-level syntax is dying, but the high-level architecture is more important than ever.
Let us talk about that architecture. Daniel mentioned context engineering. To me, that feels like the real frontier. In our current workflows, we are not just sending a single message anymore. We are feeding the AI entire libraries of data, real-time API feeds, and specific brand guidelines. Is that where the engineering part actually lives now?
Absolutely. Context engineering is the art of curate-and-provide. Instead of worrying about whether you used the word act as a lawyer or you are a legal expert, you are focused on which three hundred pages of legal precedent you are injecting into the context window. With context windows now regularly exceeding one million tokens, the challenge is not getting the model to understand the prompt. The challenge is making sure the model has the right facts to work with so it does not hallucinate based on its general training data.
It is funny you say that, because I think people still have this misconception that the AI is this all-knowing brain. But in twenty twenty-six, we treat it more like a high-speed processor. If you give it garbage context, you get garbage results, no matter how well-engineered your prompt is. I see a lot of people failing with AI right now because they are still trying to be prompt poets instead of data architects.
Prompt poets. I like that. It is true, though. There was this brief moment where people thought they could just be prompt engineers and that was a whole career. But the second-order effect we are seeing now is that domain expertise is making a massive comeback. If you do not understand the underlying subject, you cannot judge if the context you are providing is relevant, and you certainly cannot spot the subtle errors in the output.
That is a crucial point. Let us hold that thought on domain expertise, because I want to dig into how that changes the job market. But before we get too deep into the future of work, we need to take a quick break for our sponsors.
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...Alright, thanks Larry. I am not sure I want to stream my dreams to anyone, Herman. Especially not the ones about the mall Santas.
I think I will stick to my regular espresso, thanks. Anyway, back to the topic. We were talking about how the skill set is shifting toward domain expertise and architectural thinking.
Right. So, if prompt engineering as a standalone skill is fading, what are the broader skills we should be focusing on? If I am a college student or someone looking to pivot their career in twenty twenty-six, where do I put my energy?
I think the number one skill is what I call Outcome Specification. It sounds simple, but it is incredibly difficult. Most people are actually quite bad at describing exactly what a successful result looks like. They are vague. They say, write me a good report. Well, what defines good? What is the tone? Who is the audience? What are the key metrics? The ability to be hyper-specific about the desired output is a logic skill, not a language skill.
That makes a lot of sense. It is almost like a return to the fundamentals of the scientific method or even just basic management. You have to define the parameters of the problem. I also think there is a huge element of systems thinking here. We are moving away from single-shot prompts and moving toward agentic workflows. Herman, you have been playing around with these autonomous agents a lot lately. How does the prompting change when you are talking to an agent that might run for three hours and perform fifty different tasks?
Oh, it changes everything. You are not writing a prompt anymore; you are writing a constitution. You are setting the guardrails, the goals, and the communication protocols between different AI modules. For example, if I am building a research agent, I have to prompt it on how to handle conflicting information, how to verify sources, and when it should stop and ask me for clarification. That is not engineering a sentence; that is engineering a process.
So the engineering suffix that Daniel was questioning... maybe it is finally becoming accurate? It is just that we are engineering systems instead of engineering strings of text.
Exactly. I think that is the long-term play. The people who will thrive in the next few years are the ones who can look at a complex human task, break it down into its logical components, and then orchestrate a fleet of AI tools to execute those components. It requires a mix of technical literacy and deep creative intuition.
I also want to touch on the idea of verification. One of the biggest risks as AI gets better is that we become complacent. The outputs look so professional and sound so confident that we stop checking the math. In twenty twenty-six, being a master of AI tools means being a master of verification. You need to know how to use one AI to check another, or how to build automated tests to ensure the output is actually correct.
That is the big misconception. People think AI saves you from doing the work. In reality, AI shifts the work from creation to curation and validation. If you cannot validate the output, you are essentially just a glorified copy-paster. And that is a very dangerous position to be in.
It feels like we are seeing a divide. There are people who use AI to replace their thinking, and there are people who use AI to scale their thinking. The first group is the one Daniel is talking about—the ones whose skills will become redundant. The second group is the one that is becoming indispensable.
I agree. And there is a historical context here too. Think about the early days of the internet. You used to have to know specific search operators to find anything on Google. You had to use quotes and minus signs and site-specific commands. Now, Google just understands what you mean. The search engineer job disappeared, but the need for people who can navigate information and synthesize it grew exponentially. We are at that same inflection point with AI.
That is a great parallel. So, let us get practical for a second. If someone is listening to this and they feel like they have been focusing too much on the specifics of prompting, what should their next step be?
Step one: stop looking for the perfect prompt template. They are outdated within months. Instead, focus on learning the underlying logic of the models. Understand how temperature affects creativity. Understand how top-p sampling works. Understand the difference between a dense model and a mixture-of-experts model. If you understand the physics of the tool, you do not need a manual for every new version.
And step two: deepen your domain knowledge. If you are a marketer, become a better marketer. If you are a coder, learn the deep architectural patterns of software. The AI can handle the syntax, but you need to provide the soul and the strategy. I think that is the long-term skill set. It is being the human in the loop who actually knows where the loop should be going.
And maybe step three is learning to work with data. Since context engineering is so vital, knowing how to clean data, how to structure it, and how to feed it into these systems is becoming a universal skill. It is not just for data scientists anymore. If you can organize information in a way that an AI can easily digest, you are ten times more productive than someone who is just typing into a chat box.
It is interesting how this circles back to Daniel's point about the engineering label. Maybe we will stop calling it prompt engineering and start calling it something like AI Orchestration or Outcome Architecture. It feels more professional, more stable.
I like Outcome Architecture. It suggests that you are building something that lasts, rather than just throwing a dart at a moving target. And honestly, I am glad the old-school prompt engineering is dying. It was a bit of a dark art. It felt like we were trying to cast magic spells. Now, it feels more like we are finally learning how to collaborate with a new kind of intelligence.
Collaboration is the key word there. It is a partnership. The AI brings the speed and the breadth, but we bring the intent and the ethics and the ultimate judgment. That is a skill set that I do not think will decrease in relevance anytime soon.
Not at all. In fact, as the models get more powerful, the stakes for that human judgment get higher. If you give a super-intelligent system a slightly misaligned goal, the results can be catastrophic. So, being able to communicate goals clearly and safely is going to be the most important skill of the twenty-first century.
Well, this has been a fascinating deep dive. I think we have given Daniel a lot to think about. Prompt engineering might be changing, but the need for smart, curious humans to steer the ship is only growing.
Exactly. The tools change, but the mission stays the same.
So, to wrap things up, what are our big takeaways for twenty twenty-six? One: focus on the what, not just the how. Two: move from prompts to systems and agentic workflows. Three: invest in your own domain expertise because that is your ultimate filter. And four: learn the basics of data and context management.
And five: do not buy any dream-sync patches from Larry.
Definitely not. Those side effects sound like a nightmare. Anyway, thanks for joining us for another episode of My Weird Prompts. We really appreciate Daniel for sending in this topic—it gave us a lot of ground to cover.
It really did. If you enjoyed the show, make sure to follow us on Spotify and check out our website at myweirdprompts.com. We have an RSS feed there if you want to subscribe, and a contact form if you want to send us your own weird prompts.
We love hearing from you guys. We will be back next week with another deep dive into the world of AI and beyond. Until then, keep questioning the prompts.
This has been My Weird Prompts. See you next time!
Bye everyone!
