Context window

An AI’s memory is not what you think it is.

A context window is the amount of text an AI model can consider at once when generating a response. It’s like its working memory or immediate attention span for processing information.

Think of it like reading a book through a narrow tube that only shows a few pages at a time. As you move through the book, earlier pages scroll out of view while new pages appear. You can only make decisions based on what’s currently visible in the tube. If a character was introduced on page 2, and you’re now on page 50, that character effectively doesn’t exist for you anymore unless they’re mentioned again within your current view. The AI’s context window is that tube.

Understanding this concept is crucial. It’s the fundamental limitation that defines what an AI can and cannot do in a single interaction. It’s the difference between a coherent, helpful assistant and a forgetful, confused machine.

What is a context window in AI?

It’s the model’s short-term memory. Plain and simple. It’s a fixed-size buffer that holds the text you provide and the text it generates.

This is fundamentally different from human memory.

• We can recall memories from years ago, connecting distant ideas flexibly.
• An AI’s context window is a rigid, sliding frame. Information outside that frame is gone.

It’s also not a database.

• A database provides persistent, long-term storage you can query.
• A context window is temporary. Once the interaction ends, or the window slides past old information, that data is completely inaccessible to the model.

Finally, it’s not the same as training data. The training data is the entire library of books the AI read to become intelligent. The context window is the single page it’s allowed to look at right now to answer your specific question.

How does context window size affect AI performance?

Size is everything. A larger context window dramatically improves an AI’s capabilities.

With a small context window, an AI gets amnesia. You could be having a conversation, and after a few exchanges, it forgets what you said at the beginning. This leads to repetitive, irrelevant, or contradictory responses.

With a large context window, an AI becomes a powerhouse.

• Complex Problem Solving: It can analyze entire legal documents in one go, like OpenAI’s GPT-4, finding clauses and connections without needing to break the text into tiny pieces.
• Deep Document Analysis: It can read an entire book and answer nuanced questions about the plot, a feat made possible by models like Anthropic’s Claude 2 with its massive 100k token window.
• Coherent Conversations: It can maintain a long, multi-turn dialogue without losing the thread, which is essential for chatbots and virtual assistants like those powered by Meta’s Llama 2.

A bigger window means more context, which means more relevant, accurate, and coherent outputs.

What are the limitations of current context windows?

They are finite and expensive. No matter how big they get, there’s always a hard limit.

The primary limitation is computational cost. As the context window size increases, the computational power required to process it grows exponentially. This makes larger windows more expensive to run and slower to respond.

Another subtle issue is the “lost in the middle” problem. Research has shown that some models tend to pay more attention to the beginning and end of a long context, potentially ignoring crucial information buried in the middle.

And fundamentally, once the limit is reached, information must be discarded. This is an unavoidable architectural constraint.

How do context windows differ between various AI models?

They are a key point of competition. Model providers are in a constant race to expand their context windows.

• Early models might have had a context of just a few thousand tokens (roughly 1,500 words).
• GPT-4 launched with variants offering 8k and 32k token windows.
• Anthropic’s Claude pushed the boundary to 100k tokens and beyond.

The size of the context window often dictates the model’s price and its ideal use case. A model with an 8k window is great for general chat, but for summarizing a 50-page report, you’d need a model with a much larger capacity.

Why can’t AI models remember information beyond their context window?

Because, architecturally, that information ceases to exist for them. The model’s core design, the Transformer architecture, is built to process a specific sequence of tokens provided at inference time.

It’s not like a computer saving a file to a hard drive. There is no “hard drive” for conversational memory. The model processes the sequence of tokens inside the window, calculates the probabilities for the next token, and generates it. For the next step, the window might slide, and the oldest tokens are dropped. They are not stored. They are not retrievable. They are gone. It is a fundamental constraint of how the model processes information in real-time.

What technical mechanisms define a context window?

The core isn’t just about counting words; it’s about a few key technical components.

• Token-based Representation: Text is first broken down into “tokens.” These can be whole words, but more often they are subword units. For example, “context” might be one token, but “contextualize” might be two (“context” and “ualize”). The window limit is a hard count of these tokens.
• Attention Mechanisms: This is the magic that allows the model to weigh the importance of different tokens within the window. It can “pay more attention” to a user’s specific question while still considering the background information provided earlier in the context.
• Position Embeddings: These are signals that tell the model where each token is in the sequence. This is how it understands the order of words and the flow of the conversation, preventing the context from being just a jumbled bag of words.

Quick Test: Can you manage the window?

You’re building an AI app to analyze 100-page legal documents. The model you’ve chosen has a context window of 8,000 tokens (roughly 6,000 words or 12 pages). How do you handle this?

You can’t just feed the whole document in. The answer is to implement a strategy. You would likely need to break the document into smaller “chunks” that fit within the window. You could process each chunk one by one, perhaps summarizing them as you go, or use a more advanced technique like Retrieval-Augmented Generation (RAG) to find the most relevant chunks and feed only those to the model.

Questions That Move the Conversation

How are tokens counted within a context window?

Roughly, 100 tokens is about 75 words. An API call will typically tell you exactly how many tokens your input and its output used, which is critical for managing conversations and costs.

What happens when you exceed an AI model’s context window?

The most common result is truncation. The oldest information at the beginning of the conversation is simply cut off to make room for the new information. The conversation effectively starts over from that new point.

How can developers effectively manage context window limitations?

Besides chunking, developers use techniques like summarization (creating a running summary of the conversation to keep in the window) or implementing RAG systems that pull relevant data from an external source just in time.

What techniques are used to extend effective context window capacity?

Researchers are exploring new model architectures and techniques like “attention sinks” or sliding windows that help the model retain a memory of information even after it has technically left the strict context window.

How do context windows relate to AI hallucinations?

A limited context is a primary cause of hallucinations. If the model doesn’t have the necessary information within its immediate view, but is forced to provide an answer, it may invent facts or details to fill the gaps.

Why is expanding context window size technically challenging?

The standard attention mechanism has a computational complexity that scales quadratically with the sequence length. Doubling the window size doesn’t double the compute cost; it quadruples it. This makes scaling to very large windows incredibly difficult and expensive.

What’s the relationship between context window size and computational cost?

They are directly and exponentially linked. Larger context windows require more memory and processing power, which translates directly to higher API costs for users.

How do retrieval-augmented generation (RAG) systems work with context windows?

RAG is a clever workaround for limited context. Instead of stuffing a whole library into the window, RAG first searches the library (an external database) for the most relevant passages. It then “retrieves” those passages and puts them into the context window along with the user’s query. The AI gets the benefit of a huge knowledge base without needing a huge context window.

The race to build a bigger, better, and more efficient context window is one of the most important frontiers in AI. It is the race to give AI a better memory.

Did I miss a crucial point? Have a better analogy to make this stick? Let me know.