Source: Art: DALL-E/OpenAI
In the world of Large Language Models, the prompt has long been king. From meticulously designed instructions to carefully constructed examples, crafting the perfect prompt was a delicate art, requiring a deep understanding of both the task at hand and the model’s limitations. But as AI models grow in sophistication, we are now at a point of redefining the nature of LLM thinking and the nature of prompt engineering itself.
What does that mean? With the release of advanced LLMs life OpenAI’s new o1 and the evolution of inference capabilities, we’re entering a new era of interaction where prompt engineering—once an essential skill—is losing its old form and evolving into something new, streamlined, and far more powerful.
When Prompting Was an Art Form
Historically, working with AI models meant mastering the art of prompt engineering. The model wasn’t just handed a task—it was guided through it. We would craft prompts with detailed instructions, break tasks into smaller steps, and sometimes even provide multiple examples to ensure the model understood what we wanted.
Techniques like few-shot prompting and chain-of-thought reasoning emerged as power tools, especially for tasks involving complex decision-making, calculation, or nuanced judgment. For older models, telling the AI to “think step by step” or to provide “reasoning at each stage” helped bridge gaps in the model’s cognitive processes. It was akin to teaching a child: slow down, think it through, don’t rush to the conclusion.
While these techniques worked, they came with a cost—prompts grew more elaborate, often complex and verbose, demanding more and more from users. Simply put, the power was in the prompt. But today, with the launch of o1, prompting techniques must be reevaluated.
A New Prompt Dawns With the Rise of Inference
With the advent of models like o1, this careful prompting is less necessary, and in some cases, it’s actually counterproductive. We’ve moved into a world where models don’t need to be handheld through reasoning; they infer it automatically.
Advancing models like o1 are now equipped with a high degree of internal reasoning—an ability to infer, understand context, and make connections without being explicitly told to do so. This could mean that instead of crafting detailed, multi-part prompts, we can lean into simplicity.
In this new landscape, brevity and clarity can become the state of the prompt. OpenAI’s latest guidance reinforces this by advising users to keep prompts simple, direct, and free from complex, step-by-step instructions. The inference engine now does the heavy lifting, parsing meaning and generating responses with a level of contextual awareness that makes explicit reasoning steps unnecessary. Asking a model to “think step by step” is, in a sense, redundant.
It’s as though we’ve moved from driving a car with manual transmission to one with a fully automated system that anticipates every turn and maneuver before we even make them. The vehicle’s sophistication removes the need for complex intervention, allowing us to focus on the destination rather than micromanaging the journey.
Structuring, Not Engineering
So, if the intricate prompt is dying, what replaces it? A new form of interaction—one that shifts away from engineering complex prompts and toward structuring clear, minimal, and well-defined inputs.
In this new process, prompting becomes about providing the model with just enough information to understand the task without overloading it with unnecessary guidance. OpenAI encourages users to use delimiters—simple tools like quotation marks or section titles—to make prompts clearer and cleaner, rather than relying on lengthy, multi-faceted explanations.
For example, if you’re feeding the model multiple pieces of text, you don’t need to craft a narrative around them. Just clearly mark the sections, and the model can handle the rest. It’s an approach that reflects the models’ evolved capabilities—where structural clarity can matter more than instructional detail.
Less Is More
Another major shift in this new era is the way models handle contextual data. In retrieval-augmented generation (RAG), where models are given extra documents or information to reference, the old instinct might be to provide as much context as possible, ensuring the model had every potential clue at its disposal. But that’s changed, too.
Today’s advanced models don’t need mountains of information—they need precision. Overloading the model with too much context risks overcomplication and distraction. Instead, giving it the most relevant context sharpens its focus and leads to better, more accurate results.
We’re learning that less is more when it comes to context. The AI can now do more with less data, and providing it with excessive material doesn’t help—it hinders.
Can We Trust Inference?
This new chapter in AI interaction requires a different kind of trust. In the early days, prompt engineering was a way of compensating for what the models couldn’t do on their own. It was about building scaffolding to support the AI’s limitations.
Now, as we step into this next generation of models, that scaffolding is unnecessary. The AI has evolved past it. The shift from “tell me how to think” to “just tell me what you need” represents a broader leap forward in how we approach AI problem-solving.
It’s worth repeating, the prompt isn’t dead—it’s just evolved. In many instances, we’re no longer crafting detailed roadmaps for the AI to follow, but instead, presenting it with clear, direct questions and letting its internal engine of reasoning drive toward the solution.
The Human Touch Still Matters
Despite the move toward prompt minimalism, earlier techniques like detailed instructions and step-by-step prompts remain valuable, especially in creative pursuits and pushing the limits of AI capabilities. As models handle more inference internally, we shouldn’t relinquish our role as the human voice guiding the process. Our insights and creativity are essential in directing AI toward meaningful and innovative outcomes.
The Future of Prompting
So, what does the future hold? The rise of advanced AI models means that while prompt engineering, as we once knew it, is fading, it’s being replaced by something more technologically elegant—prompt minimalism. It’s a new interaction where simplicity is power, where we focus on clarity rather than complexity, and where we let the model’s expanded inference capabilities steps to the forefront. But, our human insights and perspectives will still remain an essential component.
