Why Tokenization and vocabulary in Prompt Engineering / GenAI? - Purpose & Use Cases

The Big Idea

Discover how breaking words into tiny pieces unlocks the magic of language understanding for AI!

The Scenario

Imagine trying to teach a computer to understand a whole book by reading it letter by letter without any breaks or clues.

You have to manually split sentences into words and guess meanings without any help.

The Problem

Doing this by hand is slow and confusing.

It's easy to make mistakes splitting words or missing important parts.

Without a clear list of known words, the computer gets lost and can't learn well.

The Solution

Tokenization breaks text into small, meaningful pieces automatically.

Vocabulary is the list of these pieces the computer knows.

Together, they help the computer read and understand language clearly and quickly.

Before vs After

✗ Before

text = 'Hello world'
words = []
for char in text:
    # manually guess word boundaries
    pass

✓ After

tokens = tokenizer.tokenize('Hello world')
vocab = tokenizer.get_vocab()

What It Enables

It lets machines quickly and accurately turn language into pieces they can learn from and use.

Real Life Example

When you talk to a voice assistant, tokenization helps it understand your words and respond correctly.

Key Takeaways

Tokenization splits text into manageable parts automatically.

Vocabulary is the known list of these parts for the machine.

Together, they make language easy for machines to process and learn.

Practice

1. What does tokenization do in natural language processing?

easy

A. Converts tokens into images

B. Breaks text into smaller pieces called tokens

C. Removes all punctuation from text

D. Combines multiple texts into one

Solution

1. Step 1: Understand the role of tokenization

   Tokenization splits text into smaller parts called tokens, like words or subwords.

2. Step 2: Compare options with tokenization definition

   Only Breaks text into smaller pieces called tokens correctly describes breaking text into tokens.

3. Final Answer:

   Breaks text into smaller pieces called tokens -> Option B

4. Quick Check:

   Tokenization = splitting text [OK]

Hint: Tokenization means splitting text into pieces [OK]

Common Mistakes:

• Thinking tokenization changes text to images
• Confusing tokenization with removing punctuation
• Believing tokenization merges texts

2. Which of the following is the correct way to represent a token ID in Python?

easy

A. token_id = 'word'

B. token_id = {word: 1}

C. token_id = [word]

D. token_id = 123

Solution

1. Step 1: Understand token ID representation

   Token IDs are numbers representing tokens, so they should be integers.

2. Step 2: Check each option's type

   token_id = 123 assigns an integer 123, which is correct. Others use strings, lists, or dictionaries incorrectly.

3. Final Answer:

   token_id = 123 -> Option D

4. Quick Check:

   Token ID = number [OK]

Hint: Token IDs are numbers, not words or lists [OK]

Common Mistakes:

• Using strings instead of numbers for token IDs
• Confusing token IDs with token text
• Using lists or dictionaries wrongly

3. Given the vocabulary {'hello': 1, 'world': 2, '!': 3}, what is the token ID list for the text 'hello world!'?

medium

A. [1, 2, 3]

B. [0, 1, 2]

C. ['hello', 'world', '!']

D. [3, 2, 1]

Solution

1. Step 1: Map each word to its token ID

   'hello' maps to 1, 'world' maps to 2, and '!' maps to 3 according to the vocabulary.

2. Step 2: Create the token ID list in order

   The text 'hello world!' becomes [1, 2, 3].

3. Final Answer:

   [1, 2, 3] -> Option A

4. Quick Check:

   Text tokens = [1, 2, 3] [OK]

Hint: Match words to IDs in order [OK]

Common Mistakes:

• Mixing up token order
• Using token text instead of IDs
• Assigning wrong IDs from vocabulary

4. What is wrong with this tokenization code snippet?

vocab = {'hi': 1, 'there': 2}
text = 'hi there'
tokens = [vocab[word] for word in text.split() if word in vocab]

medium

A. It will raise a KeyError if a word is missing

B. It correctly tokenizes the text

C. It ignores words not in vocabulary

D. It uses split() incorrectly on the text

Solution

1. Step 1: Analyze the list comprehension

   The code splits text and includes only words found in vocab, skipping others.

2. Step 2: Identify behavior on unknown words

   Words not in vocab are ignored, which may lose information.

3. Final Answer:

   It ignores words not in vocabulary -> Option C

4. Quick Check:

   Unknown words skipped = ignoring [OK]

Hint: Check if unknown words are skipped or cause errors [OK]

Common Mistakes:

• Assuming KeyError will happen due to 'if' check
• Thinking split() is wrong here
• Missing that unknown words are ignored silently

5. You have a vocabulary with tokens: {'I':1, 'love':2, 'AI':3, '.':4}. How would you tokenize the sentence 'I love AI!' considering the exclamation mark is not in the vocabulary?

hard

A. Add '!' to vocabulary with new ID and tokenize as [1, 2, 3, 5]

B. Replace '!' with '.' and tokenize as [1, 2, 3, 4]

C. Ignore '!' and tokenize as [1, 2, 3]

D. Raise an error because '!' is unknown

Solution

1. Step 1: Understand vocabulary coverage

   The vocabulary lacks '!', so it must be added to handle the sentence fully.

2. Step 2: Add '!' with a new token ID

   Assign '!' a new ID (e.g., 5) and tokenize the sentence as [1, 2, 3, 5].

3. Final Answer:

   Add '!' to vocabulary with new ID and tokenize as [1, 2, 3, 5] -> Option A

4. Quick Check:

   Unknown token added = new ID [OK]

Hint: Add unknown tokens to vocabulary before tokenizing [OK]

Common Mistakes:

• Ignoring unknown tokens silently
• Replacing unknown tokens incorrectly
• Assuming error without handling unknown tokens