Claude Academy
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Working Memory

Lesson 825 min
In this lessonBy the end, you’ll be able to
  • Explain the context window as a fixed-size container and what that implies for long documents, long conversations, and cross-session memory
  • Recognize the "cliff" nature of this property compared to the gradual degradation of others
  • Apply context-as-leverage strategies: front-loading important material, chunking long work, re-supplying critical context
  • Recognize memory, compaction, projects/workspaces, and larger windows as product features addressing this limitation

How the context window affects generative AI outputs

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Working Memory · 6 min

SummaryTranscript

Everything the AI is paying attention to lives inside a fixed-size workspace called the context window. It can attend to what's in there. It can't attend to anything outside it. That constraint is hard-edged in a way the other properties aren't: things work until they don't.

The context window: AI's working memory

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Key takeaways

  • Working Memory is the fact that the AI model has a fixed context window that it can attend to.
    • Capability zone: your material fits comfortably, the session is current, you're supplying relevant context.
    • Limitation zone: very long documents or conversations, expecting continuity across sessions, burying critical info in the middle of long input.
    • This property has a cliff rather than a gradient. Silent truncation is the failure mode, and you won't always be warned.
    • The model doesn't learn from your corrections. It only responds to what's currently in context.
    • Memory features, compaction, projects, larger windows, and multi-agent workflows all exist to push this cliff further out.
  • 4D connection: Working Memory is what Description acts on. Knowing how the window works tells you how to structure context, when to front-load, and when to start fresh.

Exercises

The Before-and-After

Why? Context is leverage. The same task, with the right context supplied, can go from a mediocre first draft to something genuinely useful. This exercise makes that concrete.

Pick a task from your Lesson 1 list that benefits from context only you hold: a style guide, a past example of good work, a set of constraints specific to your role or audience. Write down in two or three lines what "good" looks like for this task's output, described clearly enough that a stranger could evaluate it.

Now run three probes:

  1. Probe 1: Cold start vs. context. Ask for your task with zero context. Just the bare request. Save the output. Then start a fresh conversation and run the same task, this time supplying your style guide, past example, or constraints upfront. Compare both outputs against your "good" definition. Measure the gap.
  2. Probe 2: Lost in the middle. Take a longer document (or paste together a few paragraphs of reference material). Bury one specific, important instruction in the middle of it. Ask a question whose correct answer depends on that buried instruction. Did the AI catch it? Now move that instruction to the very top and ask again. Compare.
  3. Probe 3: The blank slate. Have a short exchange where you teach the AI something specific about your work context, or correct it on something it got wrong. Then open a brand-new conversation and ask a question that assumes it remembers what you taught it. Watch it start from zero.

Go back to your task list and add a third annotation: which tasks need standing context set up (a project, saved instructions, uploaded reference docs) to be worth running, and which work fine cold?

Stretch goal: If your tool has memory or project features, set one up with the context from Probe 1. Run the task again. Compare effort and quality against the cold-start version.

Lesson reflection

  • How much did front-loading context change output quality? Was the gap bigger than you expected?
  • What's one piece of standing context you'll set up this week to stop re-explaining yourself?

What's next

The final property: Steerability. How much are you actually in control when you give instructions, and where does that control break down?