The last two years have made one thing painfully clear: the era of clever prompt hacks is over. As models have grown more capable, more multimodal, and more deeply integrated into workflows and decision systems, prompting has evolved from a bag of tricks into a discipline.
Yet most people still approach prompting reactively, as if it were little more than phrasing instructions in a way that the model won’t misunderstand. That mindset leaves an enormous amount of potential unused. In reality, prompting in 2025 has become a method for shaping cognition itself. It’s no longer about controlling outputs; it’s about influencing the model’s internal reasoning trajectory.
Amid this shift, a deceptively simple three-part prompt framework has emerged that consistently outperforms the elaborate templates circulating online. It excels because it does not force the model into rigid formatting or artificial reasoning pathways. Instead, it guides the model into clear problem definition, expert-level simulation, and cognitive reframing — three processes that mirror how top strategists, operators, and researchers actually think.
This is what makes it durable in a landscape where everything else is changing.
Clarification as a Cognitive Gate
The first instruction in the framework is straightforward:
Ask questions until you are confident you understand the problem.
This is not a trivial request. Modern language models no longer suffer from the same naiveté that earlier models did, but they still share a common weakness: they will answer a poorly defined question confidently. By forcing the model to interrogate the problem first, you convert a passive system into an active collaborator. This step transforms ambiguity into structure.
And in a world where context engineering is becoming a critical discipline, structured clarity is the first and most valuable currency. The model’s “questions before answers” phase operates like an elite consultant’s diagnostic pass: it surfaces missing data, exposes false assumptions, and establishes boundaries before any solution is considered.
With this single instruction, you remove the largest source of model error: unclear intent. Clarification, once seen as optional politeness, has become a core mechanism for controlling reasoning depth.
Expert Simulation as a Reasoning Accelerator
The second instruction is where this framework outperforms conventional prompting:
Approach the problem as a top 0.1% expert in the field would.
This is not roleplay. It is not anthropomorphism. It is an activation cue. When models are prompted to adopt expert perspectives, they modify their reasoning pathways in ways that produce qualitatively different results. They shift from surface-level synthesis to analysis that includes constraints, trade-offs, long-term implications, and first-principles decomposition — the mental habits that distinguish top performers from everyone else.
What makes this so effective in 2025 is that the best models have been trained on increasingly diverse forms of expert discourse: research papers, postmortems, technical reviews, strategic memos, and high-level design documents. When you invoke an elite practitioner, you’re tapping into a latent network of patterns the model would otherwise underutilise.
This expert-simulation phase produces reasoning that is sharper, more grounded, and more aligned with how high-stakes decisions are made in real organisations.
Reframing as a Catalyst for Insight
The third instruction is the most underestimated and arguably the most transformative:
Reframe the problem in a way that changes the way I see the situation.
Reframing is not a trick; it is a cognitive intervention. When executed correctly, it forces the model to challenge the structure of the user’s question itself. Many of the world’s best operators, innovators, and researchers share a common skill: they solve problems by redefining them.
LLMs, when prompted directly, rarely produce this kind of insight. They follow the user’s framing too literally. But when explicitly asked to reframe, the model begins to explore the deeper landscape around the problem — the underlying incentives, the hidden constraints, the adjacent opportunities, the assumptions that everyone else is taking for granted.
Reframing turns the model from a passive responder into an engine for conceptual innovation. It unlocks nonlinear thinking, the kind that doesn’t simply optimise a path but finds a new one.
A Framework That Matches Where the Industry Is Going
What makes this three-part structure so compelling is its alignment with how AI is being deployed now. In 2025, leaders no longer want models that simply generate content. They want models that think with them: models that can participate in planning, strategy, diagnostics, R&D, scenario modelling, and systems-level evaluation.
And this is exactly where the framework excels. It does not rely on brittle formatting. It does not collapse if the model behaviour shifts slightly. It is not tied to a particular interface or model version. It rests instead on durable cognitive principles: clarity, expertise, and reframing — principles that will remain relevant no matter how the underlying algorithms evolve.
This resilience makes the framework well-suited not only for today’s LLMs but for the coming months of rapid transformation. As models grow more agentic and more deeply embedded in business infrastructure, the need for a strategic prompting foundation will only grow stronger.
Optional Extension: Introducing Systems-Level Thinking
For more advanced applications, particularly for executives, researchers, and product builders, a fourth instruction elevates the interaction even further:
Break the problem into first principles and map out second-order effects.
This addition forces the model to move beyond surface reasoning and begin evaluating the system in motion: the feedback loops, trade-offs, incentives, impacts, and emergent behaviours that define real-world outcomes. It shifts the output from tactical advice to systems intelligence.
As organisations rely more heavily on LLMs for planning and forecasting, this systems-level reasoning becomes essential.
A Tool for Strategic Cognition, Not Task Automation
The beauty of this framework is that it avoids the trap of trying to “control” the AI through increasingly elaborate instructions. Instead, it guides the model into the cognitive behaviours that produce the most valuable results: deep understanding, expert reasoning, and conceptual transformation.
In a landscape crowded with prompts that over-specify and under-deliver, this approach remains lean, elegant, and high leverage. It allows the user to interface with an LLM not as a machine to be commanded, but as a strategic partner capable of real insight.
This is the future of prompting, not as syntax, but as structured thinking.
