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LLM Lab 2: Token Budget Blitz

Manage a limited context window and see why concise prompts keep the model on target.

Published January 8, 2026

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LLM Lab 1: Next-Token Arcade

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LLM Lab 3: Prompt Anatomy Workshop

The game: stay under budget

Your prompt and the model’s reply must both fit inside the context window. Overflow means older tokens drop off.

Can You Guess?

You have a 4k-token window. You send 1k tokens of instructions and paste a 2.5k-token PDF. How many tokens remain for the answer?

How overflow happens

Token window tug-of-war

Follow the steps and watch each checkpoint light up as you progress.

1

Step 1: Prompt enters

System + user messages consume tokens immediately.

2

Step 2: Model replies

Each generated token also counts against the same window.

3

Step 3: Oldest tokens fall off

If the window is full, the oldest tokens are discarded first.

4

Step 4: Context is lost

Dropped tokens mean the model literally cannot see that part anymore.

Try this now

Trim for clarity

  • Paste a long email draft into your chat model and ask for a summary.
  • Now trim the prompt to one sentence of instructions and the email body.
  • Compare how much space the model leaves for its response.

    • Key Takeaways

    • ✓ Context windows are shared by your prompt and the model output.
    • ✓ Over budget? Old tokens fall out of view.
    • ✓ Being concise preserves room for better answers.

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    Lesson: LLM Lab 2: Token Budget Blitz

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