Most people using AI tools think about the output. Almost nobody thinks about the context.
That's the gap.
Context is what the model is working with. Every message, every file, every tool result - it all accumulates. A 200k token window sounds like unlimited space until you're 80k tokens into a debugging session and the model starts losing track of what you told it an hour ago. It's not broken. It's full.
The default behavior is to let it fill up and hope for the best. The deliberate behavior is to manage it like a resource.
Here's what that actually looks like in practice.
I run sessions that routinely hit 150k+ tokens. The things I do differently:
I treat context compaction as a tool, not a failure. When a session gets long, most people get frustrated when the model loses thread. I use auto-compact intentionally - summarize, compress, continue. The session doesn't die. It just gets a memory flush and keeps going.
I scope sub-agents for isolation. If I'm running a long orchestration task and one part of it requires deep exploration - say, reading through a codebase to understand a dependency - I don't do it in the main session. I spawn a sub-agent, let it go deep, get the answer back. The main session context stays clean.
I front-load the signal. The stuff that matters most goes in early. The model weights recent tokens more than distant ones. If there's a constraint or an invariant the agent cannot violate, I don't mention it in passing halfway through. I put it where it will be seen.
I prune aggressively. Long tool outputs, verbose API responses, redundant context - I strip it before it goes into the window. The model doesn't need the full JSON blob. It needs the three fields that matter.
None of this is complicated. It's just deliberate.
The mental model shift: context is a workspace, not a conversation log. You control what's in it. Most people don't act like they do.
The teams and engineers who figure this out early will get substantially more out of AI tools than the ones who don't - not because they have access to better models, but because they're feeding the model a better problem.