Context handoffs are where my AI spend quietly jumps

I used to think the expensive moment in AI work was the big prompt.

It usually is not.

The jump often happens at the handoff:

• pasting a long bug thread into a new task
• dragging old tool output into a fresh session
• carrying yesterday's context into today's different problem
• keeping the same large model on after the hard part is done

Big context windows make this feel free. It is not free. You pay in tokens, latency, and worse focus.

What changed for me was watching token usage live while I work.

A few things became obvious fast:

1. A clean 5 line brief beats a 500 line handoff most of the time.
2. If I cannot summarize the task simply, I am usually moving confusion, not context.
3. Handoffs should shrink the problem, not archive the whole session.
4. When the token meter jumps before the real work starts, I already know I brought too much baggage.

My current rule:

Start new tasks with the smallest context that still preserves the decision.

That has been a better cost habit than endlessly tweaking prompts.

I built TokenBar for this because postmortem spend charts tell me what happened after the waste already happened. Live token counting changes behavior in the moment.

If you work with LLMs every day, how are you handling context handoffs between tasks?

tokenbar.site