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Course contents

  • Getting Started with AI for VLSI Engineers5
  • Just Enough AI: Models and Context15
    • What a Language Model Does
    • Tokens and Tokenization
    • Context Window and Attention for Users
      • What Counts Against the Window
      • When the Window Fills
      • Attention in One Picture
      • Long Inputs and the Lost Middle
      • A Context-Budget Planner
  • Prompting13
  • Trust and Verification14
  • Using AI for Engineering Work17
  • Engineering Walkthroughs7
  • Practical Foundations for AI Workflows4
  • Engineering Data5
  • Retrieval-Augmented Generation6
  • Agents and Tool Use18
  • Agent Design8
  • MCP and Skills6
  • Multi-Agent Design6
  • Practical Capstone6
  • Tokens, Cost and Latency12
  • Agentic Workflows for Chip Teams18
  • Interview and Judgment10
  • AI Skills: Full Course Index
  • Working with AI Glossary
  • Working with AI Cheatsheet
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‹Context Window and Attention for Users
  1. Topics
  2. AI Skills for VLSI Engineers
  3. Just Enough AI: Models and Context
  4. Context Window and Attention for Users
  5. When the Window Fills

Just Enough AI · Beginner to Intermediate

When the Window Fills

What happens when input plus reserved output no longer fits the window.

On this page

    Too much input

    Nothing beyond the window is read. When input alone exceeds the budget, the tool must drop, summarize, or refuse part of it.

    The exact behavior varies by model and tool: some drop the oldest content, some compress it, some report an error. Measure it on the tool in use.

    Overflow policy

    What the model sees

    Risk

    Drop oldest

    Recent content only

    Early instructions and constraints vanish silently.

    Summarize oldest

    A compressed gist

    Details needed later are gone.

    Report an error

    Nothing until the input shrinks

    The run stops instead of degrading.

    Output spends too

    Generation also stops when the budget runs out. A reply cut off mid-answer usually means the output reserve was too small, not that the model failed.

    Long histories shrink every future answer. Each turn banks more input tokens that later turns cannot reuse for output.

    A worked overflow

    The calc block reuses the EXAMPLE plan of 2150 tokens against an EXAMPLE budget of 2000 tokens. Cost uses an EXAMPLE price of 3 units per 1 million input tokens.

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    EXAMPLE budget: 2000 tokens
    planned use: 2150 tokens
    over by 2150 - 2000 = 150 tokens
    EXAMPLE input price: 3 units per 1 million input tokens
    actual planned input = 100 + 750 + 800 = 1650 tokens
    input cost if sent and billed = 1650 x 3 / 1000000 = 0.00495 units
    the 500-token answer reserve is capacity, not billed input
    actual generated output is priced separately

    Common mistakes

    Common pitfall

    Assuming the tool keeps everything is the expensive mistake. If the tool's overflow policy drops or summarises old content, early instructions can vanish, so check the fit first.

    Interview

    Likely probes: overflow policies and the output reserve.

    • Overflow handling varies: drop oldest, summarize, or report an error.

    • A truncated answer points at the output reserve, not at reasoning.

    • History growth is input growth, so old turns eventually starve new answers.

    Check yourself An EXAMPLE plan needs 2150 tokens with a 2000-token budget. The tool drops the oldest 150 tokens. What is most likely lost?

    The earliest content: system rules or early constraints. Dropping oldest removes the head of the conversation first.

    Check yourself Should the 500-token answer reserve be charged at the input rate?

    No. The planned input is 1650 tokens, costing 0.00495 example units if sent and billed. Reserved capacity is not token usage; generated output is accounted for separately.

    Related topics

    • What counts against the window

    • Attention in one picture

    • Reducing tokens

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    See also

    • Long Inputs and the Lost Middle
    • A Context-Budget Planner