☀️ AI Morning Minute: Context Engineering
The new skill isn't writing a better prompt. It's deciding what the model gets to see.
You’ve probably heard of prompt engineering by now, (I should hope so, we’ve covered it in this newsletter). Context engineering is the bigger, more useful cousin, and it’s kinda quietly become the thing that separates an AI agent that works from one that flails. As these agents take on real jobs, the hard part stops being how you word your request. It becomes what information you hand the model before it even starts thinking.
What it means
Context engineering is the practice of deciding what goes into a model’s context window, which is basically its short-term memory for the task in front of it. That means the instructions, the documents it needs, the earlier parts of the conversation, and whatever data it pulls in along the way.
You’re building the model’s entire picture of the problem. And here’s where it gets tricky. Give it too little and it starts guessing. Give it too much and it either loses the thread or runs up a huge bill, because every scrap of context costs money to process. The whole skill is figuring out the right stuff to include and leaving the rest out.
Why it matters
Most agent screwups are context screwups, not brain screwups. The model didn’t get the one file it actually needed, or the thing it needed got buried under thirty files it didn’t. Nine times out of ten, fixing what the model sees fixes the output faster than rewording your prompt ever will.
Context windows have a ceiling, and filling them isn’t free. Every token you stuff in is a token you pay for, on every single call. Picking the right ten documents instead of dumping in a hundred is the difference between a tool that’s cheap to run and one that quietly drains your budget.
It’s turning into its own job. Teams building AI products now spend more time on what the model sees than on the model itself. That makes sense when you think about it. You can’t change how the model works, but you have total control over what you put in front of it. So that’s where the effort goes.
Simple example
A new hire starts Monday. Sharp person, learns fast, but they know nothing about how your company actually runs. You could drop every document you own on their desk and wish them luck.
Or you could pull the five files that matter for their first assignment and hand them just those. Same person. Same brain. The second version does better work, and faster, because somebody took a minute to think about what belonged on the desk and what didn’t.
The "context screwup, not brain screwup" line is the whole thing.
I build with this daily, and almost every agent failure I end up chasing is the model getting the wrong files, not the model getting dumb.
The counterintuitive part is that adding more context feels safer, so your first instinct may be to dump everything in, which almost always makes it worse.
The real skill is curation. I spend more time deciding what to leave out of the context window than what to put in. Your "new-hire-desk" example nails exactly why.