The Art of Context Engineering

There’s a great blog post by Phil Schmid called “The New Skill in AI is Not Prompting, It’s Context Engineering” , and I think he absolutely nails it. The idea is simple but powerful: to get great results from AI tools, it’s not about crafting one perfect prompt — it’s about engineering the right context. That includes giving the model relevant goals, constraints, examples, background, tone, audience… all the things that shape its understanding before it even begins to generate.

And he’s right — I’ve seen this firsthand. People new to AI often expect to type one sentence and get gold back. But the real magic happens when you take the time to build a meaningful context for the tool to operate within.

There’s just one thing I want to add to the discussion:

One of the biggest unlocks for me has been realizing that you can use AI to help you build that context.

You don’t have to start with a blank page and magically imagine the perfect setup. You can ask the model questions like:

• “What kind of context would help you perform better at this task?”
• “Can you give me a reusable template I can tweak for this use case?”
• “Here’s my goal — help me structure an effective system prompt around it.”

It sounds simple, but it changes the game. Suddenly you’re not just prompting — you’re collaborating. You’re using the tool as a context engine.

So yes, I think “context engineering” is the right frame. And I think it’s going to quietly become one of the most important skills in modern software work — not just for AI practitioners, but for anyone trying to build faster, think better, or communicate more clearly with these tools.