Stop indulging in “magic prompts” – what really determines the quality of AI output is the “information room” you build for it. This article translates the “context engineering” that has been hotly discussed in the AI circle into the vernacular: instead of repeatedly polishing a sentence, it is better to package user portraits, rules, tools, and real-time data like the chief designer and make a “project folder” that AI can understand at a glance.
Do you feel that talking to AI is like opening a blind box? When it is smooth, it quotes the classics and is amazing; When twisting, it talks nonsense and makes your blood pressure soar.
You blame yourself for not writing the prompt words well enough, so you are like an alchemist, repeatedly adjusting the “spell” in the hope of refining the “elixir”.
Have you ever thought that you may have been working hard in the “wrong” way?
The root of the problem may not lie in your “spell” at all, but in the empty “rough house” where AI is located. If you only give the AI a command but not a map, toolbox, and background information, then no matter how smart it is, it can only bump into the dark.
Forget about those “magic prompts” on the Internet, today we will talk about something more useful –Context Engineering.
From “teleprompter” to “chief designer”: what exactly is context engineering?
Let’s say you want to hire a world-class architect to design a house for you.
Prompt Engineering, like you say to the master, “I want a beautiful house.” This sentence is your hint. If you say something more specific, like “I want a modern minimalist villa with floor-to-ceiling windows and an open kitchen,” the more likely the guru will understand your intentions.
In this process, you are like a “teleprompter”, trying to influence the master’s creation with the most subtle language.
After 10 years of interaction design, why did I transfer to product manager?
After the real job transfer, I found that many jobs were still beyond my imagination. The work of a product manager is indeed more complicated. Theoretically, the work of a product manager includes all aspects of the product, from market research, user research, data analysis…
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But that’s not enough.
Context Engineering, is a completely different set of gameplay. Instead of just “saying”, you hand a project folder directly to the master.
This folder may contain:
- Your family composition and habits(User Image)
- Local building regulations and climate reporting(Rules and Restrictions)
- Photographs of various architectural styles in your collection(Successful Cases)
- A piece of land that has been geological explored with a detailed soil report(Real-time data)
- Contact details and quotation of a top construction team(Available tools/APIs)
Now what do you think can bring the master’s work closer to your dream home? The answer is self-evident.
Context engineering is the art and science of carefully designing and building this “project folder” for AI.
It is a systematic discipline that focuses on providing AI with all the context, rules, tools, and real-time data it needs to complete its tasks, presented in a structured way that is best for its understanding.
Its focus is no longer on the isolated “prompt”, but on the entire information environment in which AI is located before responding, that is, the “context”.
The 8 components of context engineering
Prompt engineering vs context engineering
Many people mistakenly believe that context engineering is just a “luxury upgrade” of prompt engineering. This understanding is like saying that architecture is just “bricklaying plus”, completely ignoring the qualitative change of its core.
Prompt engineering is the “art of points”.At its core, it is about “word formation”, and the goal is to optimize the single instruction string sent to the AI.
Context engineering is a “system of surfaces”.At its core is “information architecture”, and the goal is to design a system that can dynamically supply information.
To give a more vivid analogy:The prompt engineer is like teaching an actor to say a key line well; The context engineer is setting up the entire stage for the actor, writing the entire script, and giving the latest instructions through the teleprompter at any time.In the end, what made the actor famous in one fell swoop was the line itself, or the energy given by the entire stage and script?
Microsoft CEO Nadella repeatedly emphasized one word when explaining the concept of his Copilot product: “grounding”.
He pointed out that the value of an AI assistant depends on how well it can use your current context – the email you are writing, the report you are analyzing, the meeting you are about to attend.
This ability to “be grounded” is essentially a victory for context engineering. It transforms AI from an “understanding king” who “knows a little about everything, but doesn’t really understand anything” to a “doctoral student personal assistant” that can fit into your workflow.
Why does the future belong to contextual engineers?
If prompt engineering is the “ticket” to the AI era, then context engineering is a “first-class ticket”. The latter is much more important for three reasons:
First of all, the bottleneck of AI has shifted from “IQ” to “information”.
With the iteration of models such as Gork 4 and Gemini, the general reasoning capabilities of large language models have reached astonishing heights.
Many times, AI makes mistakes no longer because it is “stupid”, but because it “does not know”. It’s like a great detective who can’t solve a case without clues.
The failure of AI applications is increasingly manifested as “context failure”. In the future, it is no longer the model itself that determines whether an AI application is “artifact” or “tasteless”, but the quality and breadth of the context it can touch.
Secondly, the “arms race” of the context window paves the way for context engineering.
Early models could only handle the context of a few thousand words, like a goldfish with only a few minutes of memory. And now, we have a context window that can handle more than a million words (the size of a Dream of Red Mansions).
This technically opens the floodgates for building complex, stateful, and long-term memory AI applications. The explosive number of context windows is a broad stage for context engineers.
Finally, real-world business applications require “system” rather than “skill”.
A fun chatbot might be able to do so with clever prompts. However, an enterprise-level AI that can handle bank risk control, review corporate legal contracts, and manage supply chain data cannot operate stably with just a few prompts. It requires a rigorous, reliable, and scalable system to continuously supply and manage context.
Those enterprises that take the lead in mastering contextual engineering capabilities will build an insurmountable “moat”.
As Daniel Gross, the head of Y Combinator and a well-known investor, observed, the core competency of truly valuable AI agents lies in how they interact with data and tools from the outside world – this is where context engineering comes in.
Where is your location?
The fascination with “prompts” is ebating, and a deep understanding of “context” is becoming a new core competitiveness.
It’s not just something engineers and product managers need to care about, either. Whether you’re a marketer, content creator, financial analyst, or lawyer, the depth and breadth of your collaboration with AI will depend on your ability to provide context to it.
So, the next time you encounter AI “stupid”, don’t be obsessed with modifying the “spell”.
Try to take a step back and ask yourself:
- What kind of “room” did I build for it?
- In this room, do you have all the information and tools it needs to complete its mission?
- Did I give it to it with the clearest structure?
When you start thinking like a “lead designer” rather than a “teleprompter,” you’re already ahead of 99% of people.
