Transcription
Hello everyone. Today we have something truly intriguing on our agenda, a leaked system prompt for the model everyone is waiting for, GPT5. Yes, the information came from internet personality Elder Plus, and it has already been quite thoroughly analyzed, for example, on the YouTube channel AI News and Strategy: Daily Nate Be Jones. And this is, well, truly, an important thing. Exactly. We want to dive into this analysis and understand what this system prompt tells us, how it can change our interaction with ChatGPT. And most importantly, how it will differ from what we are used to, with GPT4O or, say, with Claude and Gemini.
Absolutely correct, because a system prompt is not just some accompanying text. It is, in essence, the DNA of the model, its internal instruction, defining its basic behavior. Understanding this is key to understanding how it will think and act.
So, it's like looking under the hood. Exactly. And what we see there indicates, I would say, a fundamental shift. OpenAI seems to be building not so much a conversational assistant anymore, but, well, a very active executor, an agent-like colleague. An agent-like colleague. Sounds interesting, in what specific way does this agency manifest itself, judging by the leak, the source mentions such a phrase: bias to execution. What does this mean in practice?
It means that the model is configured to immediately jump into executing a task. Right away. At most, it will ask one clarifying question, and even then, apparently, not always.
Wow. So, less dialogue, more action. Exactly. If before, with Claude or earlier versions of GPT, you could start with a general request and then iteratively refine it, have a dialogue, then the approach here is different. The model expects you to give it a clear task immediately. Okay, wait. So, if I say something like: "Help me come up with a pricing strategy for my new SaaS product." How will it react? Before, a dialogue would have started, like what is your target audience? Who are the competitors? And this new model, judging by the prompt, will most likely try to immediately provide some strategy based on its internal knowledge and possibly make some assumptions. It won't ask many questions. It will do it.
Hmm. This seems to be a double-edged sword. On the one hand, it's faster, if I know exactly what I want and can formulate it, that's great.
Yes, tasks are potentially solved much faster, less of this preliminary chatter. But on the other hand, if my initial request was inaccurate or I missed something, made a wrong assumption, the model won't re-ask, but will simply execute based on my inaccuracies.
That's the risk. The source doesn't use the metaphor "PM on crack," meaning a project manager on stimulants, for no reason. Very fast, very results-oriented, but can break things if the input data is incorrect. It turns out what they beautifully call a "beautifully looking disaster." The result will look convincing, but it will be based on incorrect premises.
Exactly. And this requires a completely new level of precision in our formulations as users. We need to be extremely specific from the very beginning.
Okay. That's the first key feature, bias to execution. What else is important in this leak? You mentioned the shift from conversations to specifications. Yes, that's the second important characteristic, closely related to the first. Since the model is not inclined to lengthy clarifications, the responsibility for the completeness and accuracy of the request falls entirely on the user. We need to learn to write not just prompts, but essentially technical specifications.
Can you give an example with that same pricing strategy, what should a correct request for GPT5 look like, if this leak is to be believed? Well, instead of the vague "help with strategy," it will be necessary to write something like: "Task: Develop three pricing strategy options for a B2B SaaS platform in the small and medium-sized business segment. Using the framework [mention specific framework if important], for each option, clearly describe the key trade-offs, for example, profitability versus market penetration speed. Result: A document of no more than 400 words in a format ready for discussion by founders. Exclude enterprise-level pricing from consideration. Do not use web search." Wow, that's a completely different level of detail, almost like setting a task for a developer or analyst.
Exactly. This requires a change in our thinking. Prompt engineering becomes closer to writing specifications. We need to think through all significant aspects of the task in advance and explicitly state them.
What elements of the request become critically important in this new approach? The source surely highlights some mandatory points.
Yes, and this is the third important feature, the structure of a correct request. You must specify several things. First, the deliverable. What exactly do we want to get as output? What format? Text, code, table, list. What length? For what audience is the result intended? Even if the audience is ourselves, it's still important to specify so that the model, for example, doesn't give too complex or, conversely, too simplified an answer.
And if I don't specify, what will happen?
The model might overdo it and give too voluminous, redundant an answer. What the source calls "over-serving." It will try to guess what we need and might give much more than was required.
I see. The result is the first thing. What else?
Second. Assumptions. This is critically important. You need to explicitly state all the context, all the frameworks, limitations, timelines that the model should rely on. It's like tying it to these conditions at the very beginning of the request.
So, if I want a market analysis, I should specify, "Assuming we only operate in the Eastern European market," or "Consider data only from the last 2 years."
Absolutely correct. Otherwise, the model might use some of its global or outdated data, and the result will be irrelevant. These assumptions set the playing field for the AI.
Third. Tools. Modern models can use external tools: internet search, code execution, image generation. And here, with GPT5, judging by the leak, you will need to explicitly indicate what the model is allowed to do and what is forbidden. Won't it decide for itself when it needs a search, for example?
That's the point. It will decide for itself if we don't instruct it otherwise. Its decision might not suit us. For example, we want it to think strategically, but instead, it will go and write code or look for ready-made solutions online. Therefore, in the request, you will need to clearly specify the tool usage policy. For example, "do not write code for this task, focus on strategic options," or conversely, "use web search to get up-to-date competitor data." So, the three pillars of a good specification for GPT5 are: result, assumptions, and tool policy.
Sounds quite structured, doesn't it? It's no longer so much about the art of crafting beautiful phrases, but an engineering approach to task definition. And the source claims that those who master this approach first will gain a certain competitive advantage.
Exactly. The model, in a way, rewards those who are ready to build immediately, who come with a clear plan. "B to build," as the source says, even if your first specification is imperfect, it will likely yield a better result than a very general, vague request requiring a long correspondence.
Practical advice from there: start practicing right now, writing specifications, even if you don't have GPT5 yet.
Yes, absolutely. Start translating your usual work processes into and from the language of such specifications. This will help develop the necessary mindset.
Speaking of tools, the analysis mentions Canvas and Memory. Are these new features in GPT? How are they related to this new approach?
Yes, these are important components of the ecosystem that will likely play a significant role. Canvas is not just a text editor for long documents. Judging by the description, it's more of an interactive environment for working on tasks collaboratively with AI.
In what sense collaboratively?
Well, imagine a workspace where you can not only write text but also iteratively develop those very specifications we talked about, create different versions, compare them, and run the model to execute different parts. The source even compares it to a version control system like Git, but for working with AI.
Sounds powerful. And what is known about Memory? Memory is a memory function. It allows you to save certain user preferences, facts about the user or project that the AI should consider in future interactions. For example, you can save: "I prefer a concise summary of three points," or "My company operates in the B2C segment."
Ah, so personalization, so that the AI better understands my context without me having to repeat it every time.
Exactly. And this fits perfectly with the idea of specifications. You can save certain basic preferences or context in Memory, and then refer to them or supplement them in a specific specification in Canvas. This makes the AI more predictable and tailored to your needs.
And how do Canvas and Memory work together? The context and your preferences, you can even collaboratively edit code or text, where the AI considers the stylistic guidelines saved in Memory, for example.
I see. So, the tools also support this shift towards more structured, engineering-based work with AI. But even with the clearest specifications, something can go wrong. What fail modes does the analysis foresee?
Yes, there are no perfect systems. The source identifies several typical problems and suggests ways to solve them. The first is speculative execution.
That's when the model does more than was asked. That same "over-serving."
Exactly, it tries to guess your unexpressed desires or complete the task and ends up doing extra work, possibly in the wrong direction. As we've already discussed, this is treated by a very clear definition of the result, as well as by adding a special section to the specification called "constraints" or "non-goals."
Non-goals. That's like you directly write, "within the scope of this task, do not do X, Y, and Z," for example. "Do not conduct a detailed financial analysis, focus only on marketing aspects." This helps to limit the model's imagination. Got it? What other failures?
The second problem is surprises with tool usage. The model suddenly starts using web search when you didn't expect it, or tries to execute code, even though the task was purely textual.
And the solution here is that very tool usage policy we talked about, right? Clearly state in the specification: "use web search only, do not execute code," or "do not use external tools at all." This removes ambiguity. Anything else?
Yes, a specific problem is mentioned: loss of comments after image generation. Apparently, in the current implementation, the system prompt is configured in such a way that after generating an image, the model might forget or omit the accompanying explanation for it.
Hmm, unexpected. And how to deal with that?
Separate the process into two steps. First, generate the image with a clear request. And then, as a second step, a separate request to analyze or describe that image. Don't try to do everything in one request. Understood? So, anticipate potential problems and proactively solve them through the structure of the specification.
Exactly, think one step ahead. If we now try to put all these pieces together – bias to execution, specifications, tools, fail modes – into a general picture, where, according to the analysts, OpenAI is moving, what is their grand strategy? This system prompt, in essence, looks like a roadmap or a declaration of intent. The goal seems very ambitious: to transform ChatGPT not just into a chatbot, but into some kind of agent-based operating system. An operating system, meaning the primary workspace for the user.
Yes? A place where you don't just get answers to questions, but manage your tasks, documents, code, plans. A system that integrates various workflows, using AI as an active agent-executor. This is already direct competition with traditional work environments, possibly even with Microsoft and Google Workspace.
And the focus, it seems, is shifting to corporate users. Very likely. Requirements for clarity, predictability, control over tools – all of this is very important in a corporate environment. Analysts expect the emergence of features related to compliance, audit trails, and the ability to configure governance controls.
So, AI is becoming more manageable, embeddable into business processes.
Yes, and likely, tools will appear for companies to better manage AI usage. Possibly some templates or tools for improving employee prompts. The source even mentions possible free training for client company employees on working with the new model.
All of this leads to the thought that the era of, you know, informal chatting with AI to solve serious problems is possibly coming to an end. The time for more formalized management and agent interaction is coming. I would say that informal communication will remain for simple tasks or idea generation, but for obtaining specific, reliable results in work, yes, a transition to a more structured approach will be required. The source even offers a kind of master template for writing specifications for GPT5.
Oh, interesting, what is its structure? It includes several sections. One: Task. A clear description of what needs to be done. Two: Deliverable. Format, length, audience, etc., as we discussed. Three: Assumptions. Context, frameworks, limitations. Four: Non-goals. What not to do. Five: Tools. Tool usage policy. Six: Acceptance criteria. How will we know that the task has been completed successfully? By what criteria will we evaluate the result? Acceptance criteria are directly from software development.
Absolutely correct. This is no longer just a prompt. It's almost a procedure, a program for AI. The thinking must shift. We are no longer just asking for help, but delegating a task to a very capable, but very literal employee. You need to think like a manager assigning a task. So, what is the very first, most practical step one can take right now to prepare for this future interaction with GPT5 or similar models?
The most useful advice from the source, in my opinion, is to take one of your most frequent, most typical work processes where AI is currently used, for example, writing emails, analyzing data, generating content, and try to rewrite the prompt for it in the form of such a detailed specification, using the proposed template. Even if you don't have access to GPT5 yet, yes, just to train yourself to think in these categories. Result, assumptions, non-goals, tools. Start creating your personal library of such prompt specifications. This will definitely not be superfluous, no matter what model appears next.
Okay, let's try to summarize. The main takeaway from the analysis of this leak: GPT5, it seems, will be an incredibly powerful tool focused on rapid task execution.
Yes, powerful, but requiring a new approach from us. We will need extreme clarity and specificity in requests, the ability to formulate tasks like a manager, not a conversational partner.
Less improvisation in dialogue, more planning and design of the request itself.
Exactly. OpenAI is creating an agent that, in the source's words, shoots first and asks questions later. Or not at all. And our task is to learn to aim its shots very precisely. This system prompt is, in essence, a declaration of OpenAI's intention to create exactly such an agent.
And here's a question that arises for me, perhaps a bit provocative for reflection. If AI becomes such an effective but literal executor that works best with clear specifications, won't this lead to it becoming more difficult to use it for purely creative, intuitive tasks?
Hmm, good question. Where there is no clear specification, where the process of search and exploration itself is non-linear, where intuition, spontaneity are important, can such an agent-executor be useful, or do we risk losing the very essence of creativity by trying to force a creative process into the framework of a specification? Where is the line between effective delegation of a task to a machine and, well, losing some important human spark in the process? How ready are we to delegate not only execution but also the task definition itself in such subtle areas? This is indeed a serious question for reflection. Perhaps different types of tasks will require different modes of interaction with AI, or even different models. But the shift towards agency and specifications is happening, and it seems to be a fact we will have to work with.
Yes, it seems we have an interesting future of interaction with artificial intelligence ahead of us. Thank you for this detailed breakdown.
You're welcome.