A few months ago, I came across the term prompt engineer — someone who crafts precise instructions for AI tools like ChatGPT or Copilot, tuning their inputs to get better outputs.
Back then, I thought, Interesting. That’s a skill.
But today, I believe that role has already evolved.
We're not just engineering prompts anymore — we're engineering context.
🛠 What Does a Context Engineer Do?
To me, a context engineer is someone who takes full ownership of the whole system. It’s no longer about getting the syntax of a prompt right. It’s about understanding the architecture, the business constraints, the flow, and then guiding the AI through that terrain like a well-briefed junior developer.
We design the mission.
We define the environment.
We decide what AI does — and more importantly, what it doesn’t do.
Let me show you what I mean.
📦 Real-Life Scenario: AI as My Coding Assistant
Suppose I need to create a new REST endpoint called createCustomer using Java and Spring Boot. Like any real-world situation, there are legal and business constraints defined by the company, and there are existing configurations and components I must respect.
Here’s the kind of prompt I give to the AI:
As a senior software engineer, your task is to create an endpoint
createCustomer. You must use full JDK21 features and best practices. To achieve this, you need to:
- Understand the endpoint by scanning the YAML file at
/aaa/bbb/ccc/myproject.yaml.- Create the
createCustomerendpoint in the controller.- Create a service class to handle this logic.
- Apply our business logic in the service layer: – Map
field1fromMySourcetofieldAinMyDest. – Mapfield2fromMySourcetofieldBinMyDest. – Use a dedicated mapper class. Check outYourMapper.javafor reference.- Call
MyRestClient: – Autowire it (already defined inMyConfig). – Make a POST call to the destination (seeapplication-local.yml). – Ensure it returns aYourDestobject as defined in the YAML.- Create another mapper class to reverse-map
YourDesttoYourSource.
I don’t write a single line of code in this process — but I design every step of how the code should be written.
Why? Because I know the system. I know the constraints. I know how to delegate effectively — even to an AI.
And because I know all that, I can be fully responsible for the result.
👨💻 I Still Review the Code — Because I’m Responsible for It
Let’s be clear: I don’t just blindly accept whatever the AI gives me.
Yes, I use AI to avoid repetition.
Yes, I let it scaffold classes, write boilerplate, and even suggest elegant solutions.
But ultimately, I own the code.
I read every single line.
If the AI comes up with something surprising — great! But I’ll ask it to explain it clearly. And if the explanation sounds too clever, I’ll say:
"Explain this to me like I’m a high school student."
If it can’t teach it clearly, it probably didn’t understand it either.
This isn't about ego. It’s about ownership.
🤖 AI Is Not Magic — It’s Your Junior Developer
Here’s what I’ve realized over time: the most productive way to work with AI is to treat it like a very fast, context-hungry, slightly naive junior dev.
It doesn't know what your business cares about.
It doesn't know your company's weird YAML structure.
It doesn't know what happened in the last project that blew up in production.
But you do.
So your job is to set the scene, break down the tasks, refer to existing assets, and guide the AI as if it were a teammate.
That’s what being a context engineer is all about.
🚀 Final Thoughts
We're entering a new phase of software engineering — one where AI isn’t just a Copilot, but a collaborator.
To thrive in this new era, we don’t need to become AI whisperers.
We need to become context architects.
People who understand the system, the goals, and how to lead a machine through the forest of complexity — without losing sight of the destination.
So no, I don’t write most of the code anymore.
But I still build.
And I still take full responsibility.
Because I’m not just prompting the AI — I’m engineering the context in which it thinks.
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