Context window and token limits in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a context window in language models?

A context window is the amount of text (tokens) a language model can look at or remember at one time to understand and generate responses.

beginner

Why do language models have token limits?

Token limits exist because models can only process a fixed number of tokens at once due to memory and computation limits.

intermediate

How does exceeding the token limit affect a model's output?

If input text is longer than the token limit, the model may ignore or cut off the extra tokens, leading to incomplete or less accurate responses.

beginner

What is a token in the context of language models?

A token is a piece of text like a word or part of a word that the model processes. For example, 'chat' and 'ting' might be two tokens for 'chatting'.

intermediate

How can you manage long texts with token limits in language models?

You can split long texts into smaller parts within the token limit or summarize parts to fit the model's context window.

What happens if a text input exceeds a model's token limit?

A The model ignores tokens beyond the limit

B The model processes all tokens anyway

C The model increases its token limit automatically

D The model crashes immediately

Models only process tokens up to their limit; extra tokens are ignored or cut off.

Which of these best describes a token?

A A single character only

B A sentence

C An entire paragraph

D A piece of text like a word or part of a word

Tokens can be words or parts of words that the model processes.

Why is the context window important for language models?

A It controls the model's training speed

B It limits how much text the model can understand at once

C It decides the model's output language

D It stores the model's parameters

The context window limits the amount of text the model can consider when generating responses.

How can you handle a text longer than the token limit?

A Split the text into smaller parts

B Ignore the token limit

C Add random tokens to the text

D Use only the first character of the text

Splitting text into smaller parts helps fit within the token limit.

What is a common reason for token limits in models?

A To make models slower

B To reduce model accuracy

C Memory and computation constraints

D To limit user input length arbitrarily

Token limits help manage memory and computation resources during processing.

Explain what a context window is and why token limits matter in language models.

Think about how much text the model can see at once and why it can't see unlimited text.

Describe strategies to work with texts longer than a model's token limit.

Consider how to prepare text so the model can handle it properly.