Context window and token limits in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the number of tokens in a text using a simple split.

Prompt Engineering / GenAI

text = "Hello world! This is a test."
tokens = text.[1]()
print(len(tokens))

Drag options to blanks, or click blank then click option'

Areplace

Bjoin

Cstrip

Dsplit

Attempts:

3 left

💡 Hint

Common Mistakes

Using join instead of split.

Trying to replace tokens instead of splitting.

The split() method splits the text into tokens (words) separated by spaces.

2fill in blank

medium

Complete the code to limit the number of tokens to a maximum context window size.

Prompt Engineering / GenAI

text = "Hello world! This is a test."
max_tokens = 5
tokens = text.split()
limited_tokens = tokens[:[1]]
print(limited_tokens)

Drag options to blanks, or click blank then click option'

A5 + 1

Blen(tokens)

Cmax_tokens

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using len(tokens) which does not limit tokens.

Using 0 which results in an empty list.

We use max_tokens to slice the tokens list and keep only the allowed number of tokens.

3fill in blank

hard

Fix the error in the code that counts tokens but mistakenly uses a wrong method.

Prompt Engineering / GenAI

text = "Sample text for token count."
token_count = len(text.[1]())
print(token_count)

Drag options to blanks, or click blank then click option'

Asplit

Bcount

Cjoin

Dreplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using count() which counts occurrences of a substring.

Using join() which combines strings.

The split() method splits the text into tokens, so counting its length gives the token count.

4fill in blank

hard

Complete the code to create a dictionary of tokens and their counts, filtering tokens longer than 3 characters.

Prompt Engineering / GenAI

text = "Hello world! This is a test."
tokens = text.split()
token_counts = {token: tokens.count(token) for token in tokens if len(token) [1] 3}
print(token_counts)

Drag options to blanks, or click blank then click option'

A:

B>

C<

D=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' instead of ':' in dictionary comprehension.

Using '<' which filters shorter tokens.

The colon : separates keys and values in a dictionary. The greater than > filters tokens longer than 3 characters.

5fill in blank

hard

Fill both blanks to create a dictionary of tokens in uppercase, their counts, and filter tokens with length less than 6.

Prompt Engineering / GenAI

text = "Hello world! This is a test."
tokens = text.split()
result = { [1] : tokens.count(token) for token in tokens if len(token) [2] 6 }
print(result)

Drag options to blanks, or click blank then click option'

Atoken.upper()

B<

Ctoken

D:

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'token' instead of 'token.upper()' for keys.

Using '>' instead of '<' for filtering.

Forgetting ':' between key and value.

We use token.upper() to convert tokens to uppercase keys, '<' to filter tokens shorter than 6, and ':' to separate keys and values in the dictionary.

Practice

1. What does the context window in a language model refer to?

easy

A. The speed at which the model generates text

B. The maximum amount of text the model can process at once

C. The number of layers in the model

D. The size of the model's vocabulary

Solution

1. Step 1: Understand the term 'context window'

   The context window is the chunk of text the model reads at one time.

2. Step 2: Relate to model processing limits

   The model cannot process more text than this window size at once.

3. Final Answer:

   The maximum amount of text the model can process at once -> Option B

4. Quick Check:

   Context window = max text processed [OK]

Hint: Context window means max text input size [OK]

Common Mistakes:

• Confusing context window with model layers
• Thinking it relates to speed
• Mixing it with vocabulary size

2. Which of the following is the correct way to check if input text fits within a model's token limit in Python?

easy

A. if len(tokenizer.encode(text)) <= token_limit:

B. if len(text) <= token_limit:

C. if len(text.split()) <= token_limit:

D. if text.length <= token_limit:

Solution

1. Step 1: Understand token counting

   Tokens are pieces of text, not just characters or words, so we must use the tokenizer.

2. Step 2: Use tokenizer to encode text

   Using tokenizer.encode(text) gives the token list; its length is token count.

3. Final Answer:

   if len(tokenizer.encode(text)) <= token_limit: -> Option A

4. Quick Check:

   Use tokenizer.encode() to count tokens [OK]

Hint: Use tokenizer.encode() to count tokens, not len(text) [OK]

Common Mistakes:

• Counting characters instead of tokens
• Counting words by splitting text
• Using incorrect syntax like text.length

3. Given a model with a token limit of 10, what will be the output of this Python code snippet?

text = "Hello world! This is AI."
tokens = tokenizer.encode(text)
print(len(tokens) <= 10)

medium

A. Error: tokenizer not defined

B. False

C. True

D. 10

Solution

1. Step 1: Check for defined variables

   The code uses tokenizer.encode(text), but tokenizer is not defined or imported.

2. Step 2: Trace execution

   Execution stops at tokens = tokenizer.encode(text) with NameError: name 'tokenizer' is not defined. No output is printed.

3. Final Answer:

   Error: tokenizer not defined -> Option A

4. Quick Check:

   Undefined tokenizer causes NameError [OK]

Hint: Check for undefined variables like tokenizer [OK]

Common Mistakes:

• Assuming tokens equal words
• Ignoring tokenizer definition
• Confusing output with token count

4. You have a model with a 50-token limit. This code throws an error. What is the likely cause?

input_text = "A very long text..."  # over 100 tokens
tokens = tokenizer.encode(input_text)
if len(tokens) > 50:
    model.generate(tokens)

medium

A. The input tokens exceed the model's token limit

B. The tokenizer.encode() function is missing parentheses

C. The if condition should be len(tokens) < 50

D. The model.generate() function cannot accept tokens directly

Solution

1. Step 1: Trace code execution flow

   Input exceeds 100 tokens, so len(tokens) > 50 is True and model.generate(tokens) executes.

2. Step 2: Check model.generate() input type

   Usually, model.generate() expects input_ids as a tensor, not raw token list from encode(), causing TypeError.

3. Final Answer:

   The model.generate() function cannot accept tokens directly -> Option D

4. Quick Check:

   model.generate() needs tensor input_ids, not list [OK]

Hint: model.generate() expects text, not token list [OK]

Common Mistakes:

• Assuming generate accepts tokens directly
• Ignoring correct token limit check
• Misreading if condition logic

5. You want to send a long document to a language model with a 1000-token limit. Which approach best ensures the model processes the entire document without errors?

hard

A. Only send the first 100 tokens to reduce load

B. Send the whole document at once and hope the model truncates it correctly

C. Split the document into chunks of 1000 tokens or less and process each separately

D. Increase the model's token limit by changing its architecture

Solution

1. Step 1: Understand token limit constraints

   The model cannot process more than 1000 tokens at once, so input must fit this limit.

2. Step 2: Choose a method to handle long text

   Splitting the document into chunks under 1000 tokens ensures all parts are processed without errors.

3. Step 3: Evaluate other options

   Sending all at once risks truncation; sending only 100 tokens loses data; changing architecture is not feasible.

4. Final Answer:

   Split the document into chunks of 1000 tokens or less and process each separately -> Option C

5. Quick Check:

   Chunking long text fits token limits [OK]

Hint: Split long text into token-sized chunks [OK]

Common Mistakes:

• Sending too long text at once
• Ignoring most of the document
• Thinking token limit can be changed easily