Tokens, context windows and why AI forgets | Master AI Automation in 4 hours Master AI Automation in 4 hours Course About Ayush Modules Sample chapter Toolbox The Microcap Minute Module 01: What AI Actually Is / Chapter 2 Tokens, context windows and why AI forgets Watch first, then read.
Same lesson, your pace.
What you will learn - What tokens are and why they matter - What a context window is, using a desk analogy - The difference between chat history, memory and knowledge The machine reads in puzzle pieces You read words.
An LLM reads tokens , chunks of letters.
A token might be a whole word ("elephant"), or a piece ("ele", "phant"), or punctuation.
Rule of thumb for English: 1 token ≈ ¾ of a word, so 1,000 words ≈ 1,300-1,500 tokens.
Two reasons: Cost and limits are counted in tokens , not words.
API bills (Module 4) charge per token.
Unusual text becomes many tokens , rare names, code, other languages, which is why some things confuse models more than others.
The desk that is only so big Imagine the model is a student answering exam questions at a desk with limited space .
Everything it may use, your whole conversation, pasted documents, its own replies, must fit on that desk at once.
That desk is the context window : the maximum amount of text (in tokens) the model can consider at one moment.
Modern windows are large (hundreds of thousands of tokens), but not infinite.
Three things follow: It only knows what's on the desk.