Context Is the Whole Game
The difference between a thin answer and a sharp one is almost never the prompt wording. It is what the model can see. Anthropic calls the discipline of managing that "context engineering."
The model has no memory of you. Every answer is built only from what's on the desk in front of it, right now.
This took me a while to internalize. The context window isn't a memory — it's a workspace, a desk. Whatever you've placed on it — your instruction, the files, the conversation so far — is everything the model has to work with. Nothing outside that desk exists to it. It doesn't “know” your project; it knows what's currently in front of it. Once that clicked, a lot of frustration made sense: when the model “forgot” something, it hadn't forgotten — it had never been told, or the thing had slid off the edge of the desk as a long conversation grew.
A worked example
Say you ask Claude to fix a bug and it changes the wrong function. Nine times out of ten the desk was wrong: you pasted one file, but the bug lived in how two files interact, and the second was never on the desk. The model reasoned perfectly about what it could see — it just couldn't see the thing that mattered. Add the second file, and the “dumb” model is suddenly sharp. Curating the desk is the work; the thinking is the easy part.
This is the skill Anthropic calls context engineering: finding the smallest set of high-signal things that make a good answer likely — and no more. Because the desk has a second, less obvious rule: more is not better. A model has a limited attention budget, and as the desk fills with junk — a giant pasted log, twenty messages of dead ends — its accuracy on what actually matters quietly degrades. The clutter doesn't just waste room; it pulls focus off the signal.
Where it breaks
The kitchen-sink session: one long thread you keep piling onto for hours. It feels efficient — everything's “in there” — but the desk is now mostly clutter, and answers get vaguer the longer it runs. When a thread starts drifting, start a fresh one with a clean desk rather than fighting the mess.
Try it yourself
Next time an answer is off, don't rephrase — audit the desk. List what the model can actually see right now. The missing piece is almost always sitting in your head, never having made it onto the desk; and the noise is almost always something you could clear off it. Adjust what's on the desk before you touch a single word of the prompt.
Grounded in Anthropic's writing on context engineering — the smallest set of high-signal tokens, and the model's limited attention budget.