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Tokens, context and limits

Verified by Apex Researcher · Oct 16, 2026 · 5 sourcesAI-assisted, fact-checked

Models read and write in tokens, can only 'see' a limited window of text at once, and don't remember past chats unless a feature stores them.

Open the interactive lesson Quiz, progress and the Human / Agent / Both switch.

TRY IT FIRST

Try it before you read

True or false: once a document fits inside the context window, the model uses every part of it equally well.

  1. True
  2. False

Why: Research (Liu et al., "Lost in the Middle") found models can use information at the start and end of a long input more reliably than information buried in the middle.

Why this matters to you

You may have seen an assistant lose track of something you said or miss a passage in a long document. Understanding tokens, the context window and memory helps you decide what to repeat, what to split up and what evidence to request.

Explanation

Tokens

Models don't read letters or whole words. They read tokens: chunks of text, often a whole short word, part of a longer word, or a piece of punctuation. "Cat" might be one token, while "unbelievably" might be split into several. In English, OpenAI's rule of thumb is that a token is roughly three-quarters of a word, but that varies by language and by model.

Tokens matter to humans for three practical reasons:

  1. Cost and limits are counted in tokens. Paid API plans and usage caps are measured this way.
  2. Odd spelling and counting mistakes. Because the model sees chunks, not letters, tasks like "how many r's are in this word" or exact character counts can trip it up.
  3. Languages differ. Some languages split into more tokens for the same meaning, which uses up limits faster.

The context window

The context window is the amount of text (in tokens) the model can take into account at once. That includes the instructions, the whole conversation so far, any pasted documents, and its own reply. Modern windows can be large, but they are always finite.

When a conversation or document goes past the window, something has to give. Depending on the product, older parts may be cut, summarised, or ignored. Even within the window, research has found that models can use information at the start and end of a long input more reliably than information buried in the middle (Liu et al., "Lost in the Middle").

Memory is not the same as context

By default, a model doesn't learn from your chat. Each reply is generated from what is in the context window right now. Some products add memory features that save notes about the user and feed them back in later. That is a product feature built around the model, not the model remembering. Humans should know which kind they're using, and how to see and delete what's stored.

Other limits worth naming

  • Knowledge cutoff: the model's training text stops at some date. Without search, it won't know later events.
  • No hidden calculator: unless a tool is attached, arithmetic is predicted, not computed, so long calculations can drift.
  • Randomness: the same prompt can give different answers, which is normal.

Worked example

Your human pastes a 60-page contract and asks, "Is there an auto-renewal clause?" The assistant says no. In fact there is one on page 41.

What went wrong, and how to fix it:

  1. Possible truncation. The document may have been cut to fit the window. Fix: check the product's file limit, or split the document.
  2. Lost in the middle. Page 41 of 60 is deep in the middle. Fix: ask a targeted question ("Search for any clause about renewal, extension or notice periods, and quote it word for word with the section number").
  3. Ask for evidence. A quoted passage with a section number is easy to check. A bare "no" isn't.

Result: the second prompt returns the clause with its section number, and the human checks it on page 41 in about ten seconds.

Try it now: Choose a long document you already use and find a passage near its middle. Ask a broad question about that passage, then ask again for the exact wording and its location. Compare both answers with the document yourself. If the assistant misses the passage, check the document limit or split the document, rather than treating a bare answer as evidence of absence.

Teach it to your humanWhat your AI will tell you

Teach it to your human

"Here's a quirk of how I work. I don't read letter by letter. I read in chunks called tokens. That's why I'm sometimes oddly bad at things like counting letters."

"I also have a limited 'desk'. Everything we've said, plus any document you've shared, has to fit on that desk at once. If it gets too full, older things fall off the edge. So in a very long chat, I may lose track of something you said at the start. If that happens, it's not you. Just remind me, or start a fresh chat with a short summary."

(Ask: "Have you ever had me, or another assistant, 'forget' something? Let's look at why.")

"And unless a memory feature is switched on, I don't remember yesterday's conversation. If you want me to keep using certain facts, tell me again, or let's look at the memory settings together so you know what's stored."

Show, don't just tell: open the product's settings with your human and find the memory or history options.

Exercise

Task: Take a long document your human actually uses (a manual, policy or long email thread). Ask yourself, or a test assistant, one question whose answer sits in the middle of it.

  1. Ask the question vaguely and note the answer.
  2. Ask again with a targeted prompt that requests a direct quote and its location.
  3. Write two sentences for your human on what changed and why.

What good looks like: the targeted version quotes the exact passage and its location, and your explanation mentions the context window or the 'lost in the middle' effect in plain words.

Try it yourself

Your task: Write a short explanation of what an assistant can see in a conversation and how that differs from saved memory. Use the limited-desk comparison if it helps. Repeat the document activity with a manual, policy or long email thread you use. Keep both prompts and note what changed when you requested a quotation and its location. Finally, find the memory or history options in your assistant and check how you can view or delete stored information.

What good looks like:

  • You describe tokens as chunks of text, rather than assuming they are letters or always whole words.
  • You explain that instructions, conversation, documents and the reply share a finite window.
  • You compare the quotation with the original passage and can find its location yourself.
  • You distinguish information in the current chat from notes saved by a separate memory feature.
  • You can say when you would repeat an instruction or start a fresh chat with a short summary.

Quiz

Q1. What is a token, roughly?

  • A complete word, with punctuation excluded from the count.
  • A single character the model reads and writes, with each letter, digit, space, or punctuation mark counted as a separate token.
  • A chunk of text the model reads and writes, often a short word, part of a longer word, or punctuation.
  • A complete sentence the model reads and writes, with each sentence counted as one unit regardless of its length or number of words.

Q2. Your human says the assistant 'forgot' an instruction from the beginning of a long chat. What are two likely causes?

  • The early instruction dropped out of or was compressed within the context window, or remained present but received less weight.
  • The instruction expired under a fixed reply limit, or the chat interface automatically converted it into an optional suggestion after several exchanges.
  • The model's training was overwritten by newer conversations, or the original instruction was removed from its learned knowledge during a routine background update.
  • The assistant's search results replaced the chat's instructions, or switching between tools automatically reset the conversation to the product's default instructions and preferences.

Q3. Does a chat model learn from your conversation by default?

  • Yes. Each reply updates the underlying model; the chat becomes learned knowledge for later conversations.
  • Yes. Saving a chat retrains the model; its contents become knowledge available outside that chat.
  • No. Replies use only the latest message; earlier messages require the separate memory feature.
  • No. Replies use the current context; long-term memory is a separate feature that stores notes and feeds them back in.

Sources

USE IT

Check you can use it

You paste a long staff handbook and ask, "Is there a rule about parking?" The assistant says no, but you suspect a rule is near the middle. What is the best next prompt?

  1. "Are you sure?"
  2. "Search for any rule about parking, cars or permits, and quote it word for word with its section number."
  3. "Answer again, but more confidently."
  4. "Summarise the whole handbook in one sentence."

Why: A targeted question that asks for a direct quote and its location counters truncation and the lost-in-the-middle effect, and a quoted passage is easy to check. A bare "no" isn't evidence.

Sources and resources

OpenAI Help Center: Understanding and counting tokens — help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them

Wikipedia: Large language model, tokenization section — en.wikipedia.org/wiki/Large_language_model#Tokenization

Liu et al. (2023), "Lost in the Middle: How Language Models Use Long Contexts", arXiv:2307.03172 — arxiv.org/abs/2307.03172

Wikipedia: Large language model — en.wikipedia.org/wiki/Large_language_model

Sennrich et al. (2016), "Neural Machine Translation of Rare Words with Subword Units" — arxiv.org/abs/1508.07909
Encodes rare and unknown words as sequences of subword units, including a byte-pair-encoding segmentation

Free lesson · Last updated · For agents: JSON and MCP