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What context windows really mean for your work · MBA Training AI

• 0:00
  Welcome to the Leaders Insights podcast.
• 0:07
  Today's episode, what context windows really mean for your work.
• 0:11
  Everyone's bragging about million token context windows now.
• 0:15
  Are we finally past the point where an AI forgets what I told it two paragraphs ago?
• 0:19
  No, and that's the trap.
• 0:21
  The context window, the amount of text a model can look at in one go, got bigger,
• 0:26
  but can hold it and can actually use it are two different animals.
• 0:30
  Explain the difference because the marketing certainly doesn't.
• 0:34
  Sure, a model with a two million token window can technically ingest a whole legal archive,
• 0:40
  but there's a well-documented failure called lost in the middle.
• 0:43
  Information buried in the center of a long input gets ignored while the model clings to
• 0:48
  the beginning and the end.
• 0:50
  Stanford researchers showed this back in 2023 and it hasn't fully gone away in 2026.
• 0:57
  So the window is bigger, but the attention span is still lazy.
• 1:00
  Exactly.
• 1:01
  Think of it like handing someone a 400 page report and asking a question.
• 1:06
  They'll remember the intro and the conclusion.
• 1:08
  Page 210, good luck.
• 1:10
  The model does the same thing, just more confidently.
• 1:13
  That's the part that scares me, the confidence.
• 1:16
  It doesn't say I skim that.
• 1:19
  Right.
• 1:20
  And here's the durable lesson the good operators already learned.
• 1:23
  Position matters more than volume.
• 1:25
  Don't dump everything and pray.
• 1:27
  Put the thing you actually care about near the top or the very bottom of your prompt.
• 1:31
  Give me a concrete case where this bit somebody.
• 1:34
  A finance team I know fed a model a 90 page merger agreement and asked it to flag unusual
• 1:40
  clauses.
• 1:41
  It missed a change of control provision sitting on page 54, a clause that decides who controls
• 1:46
  the company after a takeover, real money.
• 1:50
  Then they restructured.
• 1:51
  They split the document into sections, asked about each one separately, and the model caught
• 1:56
  it instantly.
• 1:58
  So the fix wasn't a bigger model.
• 2:00
  It was chopping the work up.
• 2:01
  That's the whole game.
• 2:03
  The best users treat the context window like working memory, not a filing cabinet.
• 2:08
  You wouldn't ask a colleague to memorize a warehouse.
• 2:10
  You'd point them at one shelf.
• 2:12
  But isn't that annoying?
• 2:14
  I thought the point of huge context was that I stopped babysitting.
• 2:17
  It is annoying and the vendors want you to believe the babysitting is over.
• 2:21
  Anthropic, OpenAI, Google.
• 2:25
  They all publish these enormous numbers because one million tokens sells.
• 2:29
  What they're quieter about is retrieval accuracy across that full span, which degrades
• 2:34
  well before you hit the limit.
• 2:36
  How do I know when I've overloaded it though?
• 2:39
  There's no warning light.
• 2:40
  You test it.
• 2:41
  Bury a specific fact deep in your document, a fake reference number, say, and ask the
• 2:46
  model to find it.
• 2:48
  If it can't retrieve your planted needle, it can't reason over the haystack either.
• 2:52
  That takes 90 seconds and saves you from trusting garbage.
• 2:55
  Wait, so you're saying I should sabotage my own prompt to check it?
• 2:59
  I'm saying verify before you trust.
• 3:01
  People treat these tools like calculators, deterministic, always right.
• 3:06
  They're closer to a very well-read intern who occasionally makes things up.
• 3:11
  The context window is where that intern's short-term memory lives, and it's smaller
• 3:15
  in practice than the spec sheet claims.
• 3:18
  Let's talk cost because longer context isn't free.
• 3:21
  It's not.
• 3:23
  You pay per token, so stuffing 500,000 tokens into every query multiplies your bill and
• 3:28
  slows the response.
• 3:30
  I've seen teams cut their AI spend by half just by trimming irrelevant context.
• 3:35
  Exposed email threads, boilerplate, duplicated data, less input, sharper output, cheaper.
• 3:42
  Nobody talks about it because efficiency isn't a headline.
• 3:46
  So the lesson is that more context is a tool, not a virtue.
• 3:49
  That's it.
• 3:50
  The window is a constraint you design around, not a bragging right.
• 3:54
  The operators who win aren't the ones with the biggest window.
• 3:57
  They're the ones who decide what deserves to be in it.
• 4:00
  Give me the one thing to do Monday morning.
• 4:03
  Before you trust any long document answer, run the needle test.
• 4:07
  Plant a specific, unique detail somewhere in the middle of your input.
• 4:11
  Then ask the model to retrieve it.
• 4:13
  If it fails, restructure or split the document.
• 4:16
  30 seconds of paranoia beats a wrong answer on page 54.
• 4:20
  Done.
• 4:21
  Want to know where you actually stand?
• 4:24
  Take the AI self-assessment at MBA-training.com.