Your AI Keeps Forgetting? Here's the Secret Reason Why (It's Called a Context Window)

Ever been deep in a chat with an AI, laying out the perfect plan, only for it to suddenly ask, "What were we talking about?"

Frustrating, right? It feels like you're talking to a genius with a case of amnesia.

You're not going crazy. And the AI isn't broken.

The reason this happens is one of the most important—and most misunderstood—concepts in AI today: the context window.

Think of it as the AI's short-term memory.

Get this right, and you'll go from a confused user to someone who can command AI with precision. You'll know exactly why your AI is "forgetting" things and how to make sure it remembers what matters most.

Let's pull back the curtain.

So, What Exactly IS a Context Window?

Imagine you're baking a cake and can only look at one recipe card at a time. The card in your hand is your "context window." It has the immediate steps you need.

If you need to remember how you prepped the flour ten steps ago, you have to hope that information is still on the current card. If it's not? You've forgotten.

An LLM's context window is exactly like that. It's the maximum amount of information the AI can "see" or "remember" at any single moment.

This "memory" includes everything:

• Your initial prompt
• The entire conversation history (up to a point)
• Any documents you've attached
• The AI's own previous responses

It's the AI's working brain space. If information falls out of this window, for the AI, it never existed.

Why Size Matters: The Bigger the Window, The Better the View?

Context windows are measured in tokens.

Hold on, what's a token?

A token isn't a word. It's a piece of a word. The AI breaks down all text into these little chunks.

• "Hello" = 1 token
• "I'm" = 1 token
• "Unforgettable" = 3 tokens ("Un", "forget", "table")

As a rough rule of thumb, 1,000 tokens is about 750 words.

So, a model with a 4,000-token (4k) context window can remember about 3,000 words of conversation. A model with a 128,000-token (128k) context window can remember a whole book (around 96,000 words).

You can see why a bigger window seems better. It allows the AI to:

• Hold longer, more detailed conversations without forgetting the beginning.
• Analyze huge documents, like a 100-page report or an entire codebase.
• Synthesize information from multiple sources you've provided in the chat.

You can read more about token in our other blog [link].

What's Actually Inside That Window? (It's More Than You Think)

When you send a prompt, you're not just sending your latest message. The application (like ChatGPT, Claude, etc.) bundles up a package of information and sends it to the model. This package has to fit inside the context window.

Here’s what’s usually included:

1. System Prompt: A hidden set of instructions that tells the AI how to behave (e.g., "You are a helpful assistant."). This eats up tokens before you even start.
2. Conversation History: Every back-and-forth you've had in the current chat. The newest messages are at the bottom, the oldest at the top.
3. Tool Calls (The Secret Sauce): If the AI needs to browse the web or run some code, the request and the results from that "tool" are stuffed into the context. This can consume a LOT of tokens.
4. RAG Output (Your AI's Cheat Sheet): RAG stands for Retrieval-Augmented Generation. It's a clever trick where the system "retrieves" relevant info from a database (like a PDF you uploaded) and adds it to the context. This gives the AI facts it wasn't trained on. That entire chunk of retrieved text? Yep, it goes into the window.
5. Your Actual Question: Finally, your new prompt is added at the very end.

All of this gets bundled together. If the total number of tokens exceeds the model's limit, something has to give.

What Happens When I Cross the Context Window?

This is the moment your AI gets amnesia.

When the conversation gets too long, the system starts deleting the oldest messages from the top of the context window to make room for your new one.

It's a "first-in, first-out" system.

• You: "Hey, let's plan a trip to Japan. We need to focus on Tokyo, Kyoto, and Osaka. Our budget is $5,000."
• AI: "Great! Let's start with Tokyo..."
• (30 messages later, you've planned out Tokyo and Kyoto in detail)
• You: "Okay, now for the last city. What was our total budget again?"
• AI: "I'm sorry, I don't have information about a budget. Could you please provide it?"

The AI isn't being dumb. The message with the budget information was at the very beginning of the chat. It got pushed out of the context window to make space for the 30 messages that followed. It's gone. Forgotten forever.

Okay, How is a Context Window Measured?

As we covered, it's all about tokens.

You can't just count the words. Different models use different "tokenizers" (the tool that splits text into tokens). The same sentence can be a different number of tokens for different models.

Most AI platforms have a token counter tool you can use, or you can find them online. For example, OpenAI has a popular one called tiktoken.

Why Do Models Even Have a Maximum Context Length?

Why not just give them infinite memory? Two big reasons: Cost and Computation.

The core technology behind modern LLMs is the "attention mechanism." It's how the AI weighs the importance of every token in relation to every other token in the context window.

This process is computationally brutal. The amount of computation required grows quadratically (O(n^2)) with the number of tokens.

What does that mean in plain English?

• If you double the context window (e.g., from 4k to 8k), you quadruple (4x) the processing power and memory needed.
• If you go from 4k to 128k (a 32x increase), the computational cost explodes by 1024x.

This is why bigger context windows lead to slower responses and are much more expensive to run. The developers have to draw a line somewhere.

Why Are Context Windows Often Powers of 2? (2k, 4k, 8k, 32k, 128k)

This isn't a coincidence! It comes from the way computer hardware is designed.

Computer memory (RAM and GPU VRAM) is structured and allocated in binary—powers of 2. Aligning the context window size to these numbers (like 1024, 2048, 4096, etc.) makes the data fit more neatly into the hardware. It's like building with LEGOs that are all standard sizes—everything just clicks together more efficiently. This optimization helps maximize performance and reduce wasted resources.

How Does This Relate to the "Attention Matrix"?

Let's get slightly more technical for a second, but stick with me.

Imagine an Excel spreadsheet. The rows are all the tokens in the context window, and the columns are also all the tokens. This is the attention matrix.

For each token, the AI calculates a "self attention score" for every other token in the window. A high score means "these two tokens are highly related."

• In the sentence "The robot picked up the red ball," the AI would create high attention scores between "robot" and "picked up," and between "red" and "ball."

This matrix is what allows the AI to understand grammar, relationships, and context. But as you can imagine, a spreadsheet with 128,000 rows and 128,000 columns is astronomically huge. That's the quadratic scaling problem in action.

How Can I Know the Context Window of an LLM?

This is usually a headline feature! When a new model is released, the company will boast about its context window size. You can typically find it:

• On the official blog post or press release for the model.
• In the API documentation for developers.
• On model comparison websites and leaderboards.

The Big Debate: Huge vs. Small Context Window?

Bigger is always better, right? Not so fast. It's a classic trade-off.

Large Context Window

Pros of a HUGE Context Window (100k+)	Cons of a HUGE Context Window
Analyze Whole Books/Codebases: You can drop in massive amounts of text and ask questions about it.	Higher Cost: API calls are significantly more expensive.
Flawless Long-Term Memory: The AI can remember conversations from hours ago.	Slower Responses: It takes the AI longer to "read" all the context and generate a response.
Complex Reasoning: Can connect ideas from the beginning, middle, and end of a long document.	"Lost in the Middle" Problem: The AI can sometimes ignore information buried in the middle of a huge context.
Simpler for Users: You don't need fancy tricks; just dump all the info in.	Risk of "Context Stuffing": Giving the AI irrelevant information can actually confuse it and lead to worse results.

Small Context Window

Pros of a SMALL Context Window (4k-16k)	Cons of a SMALL Context Window
Cheaper and Faster: Much more affordable and quicker for most everyday tasks.	Amnesia: Forgets the start of long conversations.
Focused Responses: The AI is only looking at the most recent, relevant info.	Can't Handle Large Docs: You have to break up long texts into smaller "chunks" manually.
Lower Resource Usage: Easier to run on less powerful hardware.	Requires More Skill: You need to be good at summarizing and managing the context yourself.

The Verdict:

• For quick chats, writing emails, or simple Q&A, a smaller window is often better (faster and cheaper).
• For deep analysis of legal documents, querying a whole codebase, or maintaining a very long, complex character persona, a huge window is essential.

The "Needle in a Haystack" Test

How do we know if models with huge context windows are actually paying attention to all that text? Researchers came up with a clever test.

1. The "Haystack": They take a massive amount of text (like the complete works of Shakespeare).
2. The "Needle": They insert a tiny, random sentence somewhere inside it, like "The best way to make a pizza is to use pineapple."
3. The Test: They ask the AI: "What is the best way to make a pizza?"

If the AI can find and repeat that one sentence buried in hundreds of thousands of words, it has excellent "recall" ability. If it can't, it might be "lost in the middle."

Google's Gemini 1.5 Pro famously aced this test with a 1 million token context window, finding the needle every single time.

Which LLMs have the highest Context Window?

The landscape is always changing, but here are some of the current champions:

• Magic.dev LTM-2-Mini: Although experimental, supports 100 million tokens.
• Meta Llama 4 Scout: Supports 10 million tokens.
• Meta Llama 4 Maverick, OpenAI GPT-4.1, Google Gemini 2.5 Flash/Pro: supports 1 million tokens.
• Anthropic Claude 4, Claude 3.7/3.5, OpenAI o3/o4: 200,000 tokens.
• OpenAI GPT-4o, Mistral Large 2, DeepSeek R1/V3, IBM Granite: 128,000 tokens.

How Do I Reduce My Context Window Usage (and Save Money)?

You don't always need a million tokens. Being efficient is a superpower.

1. Be Concise: Get to the point. Don't write a 500-word preamble if 50 words will do.
2. Summarize: If a conversation is getting long, ask the AI to summarize the key points so far. You can then start a new chat with that summary as the context.
3. Chunking: For large documents, break them into smaller sections (chunks). Feed them to the AI one by one, or use a RAG system that can find the most relevant chunks for your question.
4. Edit Your Prompts: If the AI is including irrelevant parts of the conversation, manually edit your prompt to remove them before sending.

The Future: Is Infinite Memory on the Horizon?

The race for bigger context windows is on, but the quadratic scaling problem is a huge barrier. Researchers are working on clever ways to get around it:

• Smarter Attention: Developing new attention mechanisms that aren't quadratic, like "linear attention," which scales more gracefully.
• Context Compression: Creating techniques to "zip" the context, keeping the important information while throwing away the fluff, before sending it to the model.
• Hybrid Memory: Building systems where the LLM has both a fast, short-term context window and a slower, long-term memory database it can query.

The goal is to give the AI the illusion of infinite memory, even if the underlying tech is still limited.

Conclusion: You're Now in Control

The context window isn't just a technical term; it's the fundamental limitation that defines how we interact with AI.

It's the reason your AI forgets, the reason it can be slow, and the reason it costs what it does.

But now, the mystery is gone. You know the secret.

You can now diagnose why a conversation is going off the rails. You can choose the right model for your task—balancing power with price. And you can craft your prompts to work with the AI's memory, not against it.

You've leveled up. So, the next time you chat with an AI, what will you do differently?