What Is a Context Window — And Why You're Probably Filling It Wrong

Two million tokens. It's a headline that gets AI enthusiasts excited,and rightfully so. But if your first instinct when you see a bigger context window is to load more information into AI, you're already making the most common mistake in the book.

In Episode 109 of Automate Your Agency, Alane Boyd and Micah Johnson break down what context windows actually are, why they matter more than most people realize, and how to use them strategically instead of recklessly.

What Is a Context Window, Really?

At its most basic, a context window is AI's short-term memory. It's the total amount of information: your inputs, AI's outputs, its internal "thinking," and any loaded files or tools that the model can hold in active memory at one time.

Early versions of GPT had context windows of around 32,000 tokens. That felt massive at the time. Now we're looking at models with 1 million, even 2 million token context windows. Progress has been fast.

But here's the thing nobody tells you: when the context window fills up, AI doesn't stop and say, "Hey, I'm out of room." It just starts dropping older information, and it doesn't know it's doing it. That's where hallucinations come from. Not malice, not a glitch; just AI filling in the gaps with its best guess when the real information has fallen out of its working memory.

The Desk Analogy

Micah introduced an analogy in this episode that makes the whole concept immediately click: think of your context window as a desk.

A two-million-token context window is a genuinely enormous desk. But if you cover every inch of that desk with papers, folders, monitors, and equipment, there's no room left to actually do the work.

That's the mistake most AI users make. They see a big number and think, "Great, now I can feed AI everything about my business." But the context window isn't storage, it's workspace. You have to leave room for AI to process, think, and output. Fill it to the brim and the quality of what you get back plummets.

Why This Causes Hallucinations

Hallucinations are one of the most frustrating and misunderstood aspects of working with AI. Most people blame the model. The real culprit is almost always a context window issue.

When AI runs out of working memory, it doesn't flag it. It doesn't pause. It keeps responding, but now it's guessing. It might forget the original instructions you gave it at the start of the conversation. It might lose key details from the middle. And because it has no awareness of what it's forgotten, it fills those gaps confidently.

This is why long, sprawling AI conversations often start strong and degrade over time. The further you get from the beginning, the more the early context has been pushed out of the window.

The fix? Start new sessions more often. Use your file system to let AI load what it needs, when it needs it, rather than dumping everything in at once.

The CLAUDE.md File: Your Secret Weapon

If you're using Claude Cowork (or any AI with file system access), setting up a CLAUDE.md file is one of the highest-leverage things you can do for your context efficiency.

Instead of pasting your business context, SOPs, tone guidelines, and preferences into every conversation, the CLAUDE.md file sits in your file system. AI knows exactly where to find it. It can load just what it needs, when it needs it, rather than clogging up your context window with everything upfront.

Alane put it plainly: "It gives you more wiggle room for your output." That's the goal. Less context overhead, more room to do the actual work.

Subagents: A Smarter Way to Handle Big Tasks

One of the more fascinating developments in AI tools like Claude Cowork is the use of subagents, and how they interact with context windows.

When you give Claude Cowork a complex task, it's smart enough to spin up multiple subagents to handle different pieces of the work. Each of those subagents has its own context window. So instead of one million-token workspace, you might effectively have three, four, or six working in parallel.

Each subagent processes its chunk, summarizes the result, and feeds just the essentials back to the main thread. The main context window stays clean. The subagents do the heavy lifting in their own space. You get better results without blowing your token budget.

In the Cowork interface, you can actually watch this happen in real time: small pulsing boxes representing each active agent, checking off as they complete their work. It's one of those moments where the technology goes from impressive to genuinely remarkable.

Where Context Windows Are Headed

Alane drew a comparison in this episode that resonated: the evolution of mobile phone plans.

Remember paying per text message? Then per minute? Then data caps with overage charges? Then unlimited, but throttled? Then truly unlimited?

That's exactly the trajectory context windows are on. Right now, we're in the caps-and-limits era. Different AI providers offer different token limits at different price points. It feels chaotic because it is, they're all competing and figuring it out.

Eventually, it'll settle into a standard utility cost. You'll pay a flat rate, get "unlimited" context (or effectively unlimited for everyday use), and stop thinking about it entirely. Just like you don't think about hard drive space or mobile data anymore.

But we're not there yet. Today, being thoughtful about context windows is still a real competitive advantage.

The Takeaway

Bigger context windows are genuinely exciting. Two million tokens is a meaningful leap. But the mindset shift that matters most isn't "now I can load more" — it's "now I have more workspace, and I should still use it wisely."

• Don't fill the context window. Leave room for AI to think.
• Set up your file system. Let AI load what it needs dynamically.
• Use subagents. They're built exactly for this problem.
• Start fresh sessions before things get too cluttered.
• Stop blaming AI when the real issue is user error.

If you want the full breakdown: including the desk analogy, the mobile phone parallel, and a step-by-step walkthrough of how to set up your CLAUDE.md file. Listen to Episode 109 of Automate Your Agency. And if you haven't grabbed the free Cowork Masterclass yet, head to biggestgoal.ai, zero excuses.

Two million token context window, but if you're stuffing it full of information and hoping for the best, you're doing it wrong. Alane Boyd and Micah Johnson demystify one of the most misunderstood concepts in AI and explain exactly why bigger doesn't always mean better.

If you've ever gotten frustrated that AI "lost the plot" mid-conversation or started giving you answers that made no sense, the context window is almost certainly the culprit. Most business owners don't realize that when AI's working memory runs out, it starts guessing; and it doesn't even know it's happening.

In this episode, you'll learn:

• What a context window actually is — AI's short-term memory, and why it forgets without warning
• The desk analogy that finally makes context windows click — and why leaving space matters more than filling it
• Why hallucinations happen when the context window overflows — and how to prevent them
• How Claude Cowork's subagents spin up their own context windows to handle complex tasks smarter
• Why your CLAUDE.md file and folder system are the most underrated tools for context efficiency
• Where context windows are headed — and the mobile phone plan evolution that tells the whole story

If you're ready to stop blaming AI for bad outputs and start understanding how it actually works, press play now... your results will never be the same.