Text embedding models in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a text embedding model?

A text embedding model converts words or sentences into numbers (vectors) that computers can understand. These numbers capture the meaning of the text in a way that similar texts have similar numbers.

beginner

Why do we use text embeddings in machine learning?

We use text embeddings to turn text into numbers so machines can process and compare text easily. This helps in tasks like search, recommendation, and understanding language.

intermediate

Name two popular types of text embedding models.

Two popular types are Word2Vec, which creates embeddings for individual words, and Sentence Transformers, which create embeddings for whole sentences or paragraphs.

intermediate

How does a text embedding model help in finding similar sentences?

The model turns sentences into vectors. Sentences with similar meanings have vectors close to each other. We measure closeness using math tools like cosine similarity.

intermediate

What is cosine similarity and why is it used with embeddings?

Cosine similarity measures how close two vectors point in the same direction. It helps compare text embeddings to find how similar two texts are, ignoring their length.

What does a text embedding model output?

A A corrected spelling of the text

B A translated version of the text

C A summary of the text

D A list of numbers representing the text

Text embedding models output vectors (lists of numbers) that represent the meaning of the text.

Which of these is a common use of text embeddings?

A Finding similar sentences

B Image classification

C Audio recording

D Video editing

Text embeddings help find similar sentences by comparing their vector representations.

What does cosine similarity measure?

A The distance between two points on a map

B The angle between two vectors

C The length of a vector

D The number of words in a sentence

Cosine similarity measures the angle between two vectors to see how similar their directions are.

Which model creates embeddings for whole sentences?

A Sentence Transformers

B Word2Vec

C K-Means

D Decision Trees

Sentence Transformers create embeddings for entire sentences or paragraphs.

Why do we convert text into numbers using embeddings?

A To print text faster

B To make text colorful

C Because computers only understand numbers

D To translate text into other languages

Computers process numbers, so converting text into numbers helps machines understand and work with language.

Explain in your own words what a text embedding model does and why it is useful.

Think about how you might explain turning words into numbers to a friend.

Describe how cosine similarity works with text embeddings to find similar sentences.

Imagine comparing directions of arrows to see if they point the same way.

Practice

1. What is the main purpose of a text embedding model?

easy

A. To convert text into numbers that capture its meaning

B. To translate text from one language to another

C. To generate images from text descriptions

D. To count the number of words in a text

Solution

1. Step 1: Understand what text embedding models do

   Text embedding models turn words or sentences into number arrays that represent their meaning.

2. Step 2: Compare options with this understanding

   Only To convert text into numbers that capture its meaning describes converting text into meaningful numbers. Other options describe different tasks.

3. Final Answer:

   To convert text into numbers that capture its meaning -> Option A

4. Quick Check:

   Text embedding = convert text to meaningful numbers [OK]

Hint: Remember: embeddings turn text into numbers for meaning [OK]

Common Mistakes:

• Confusing embeddings with translation
• Thinking embeddings generate images
• Assuming embeddings just count words

2. Which of the following is the correct way to get an embedding vector from a text using a Python function get_embedding(text)?

easy

A. embedding = get_embedding->text

B. embedding = get_embedding[text]

C. embedding = get_embedding{text}

D. embedding = get_embedding(text)

Solution

1. Step 1: Recall Python function call syntax

   In Python, functions are called with parentheses and arguments inside, like func(arg).

2. Step 2: Match syntax with options

   Only embedding = get_embedding(text) uses parentheses correctly. Options A, B, and C use invalid syntax for function calls.

3. Final Answer:

   embedding = get_embedding(text) -> Option D

4. Quick Check:

   Function call uses parentheses () [OK]

Hint: Use parentheses () to call functions in Python [OK]

Common Mistakes:

• Using square brackets [] instead of parentheses
• Using curly braces {} instead of parentheses
• Using arrow -> instead of parentheses

3. Given the code below, what will be the output?

def dummy_embedding(text):
    return [len(text), sum(ord(c) for c in text) % 100]

result = dummy_embedding('cat')
print(result)

medium

A. [3, 12]

B. [3, 15]

C. [4, 30]

D. [3, 30]

Solution

1. Step 1: Calculate length of 'cat'

   The word 'cat' has 3 characters, so first element is 3.

2. Step 2: Calculate sum of ASCII codes modulo 100

   ord('c')=99, ord('a')=97, ord('t')=116; sum=99+97+116=312; 312 % 100 = 12.

3. Step 3: Determine output

   return [3, 12], so print([3, 12]).

4. Final Answer:

   [3, 12] -> Option A

5. Quick Check:

   len('cat')=3, (99+97+116)%100=12 [OK]

Hint: Calculate length and ASCII sum mod 100 carefully [OK]

Common Mistakes:

• Wrong ASCII sum calculation
• Miscounting string length
• Mixing uppercase and lowercase ASCII codes

4. The following code tries to get embeddings for two texts but doesn't work as intended. What is the problem?

def get_embedding(text):
    return [len(text)]

texts = ['hello', 'world']
embeddings = []
for t in texts:
    embeddings.append(get_embedding)
print(embeddings)

medium

A. The list texts is empty

B. The function is not called; it appends the function itself

C. The variable embeddings is not defined

D. The function get_embedding has wrong syntax

Solution

1. Step 1: Check the loop appending embeddings

   The code appends get_embedding without parentheses, so it adds the function object, not the result.

2. Step 2: Understand the problem

   Appending the function itself causes the list to hold function references, not embedding lists like [5] and [5].

3. Final Answer:

   The function is not called; it appends the function itself -> Option B

4. Quick Check:

   Missing () calls function, else appends function object [OK]

Hint: Add () to call function, not just reference it [OK]

Common Mistakes:

• Forgetting parentheses to call function
• Assuming list is empty causes error
• Thinking variable is undefined

5. You want to find the most similar sentence to 'I love apples' from a list using embeddings. Which approach is best?

hard

A. Count common words between 'I love apples' and each sentence

B. Translate all sentences to another language and compare lengths

C. Compute embeddings for all sentences, then find the one with smallest distance to 'I love apples' embedding

D. Randomly pick a sentence from the list

Solution

1. Step 1: Understand similarity with embeddings

   Embeddings turn sentences into number arrays capturing meaning, so comparing distances between embeddings finds similar sentences.

2. Step 2: Evaluate options for similarity search

   Compute embeddings for all sentences, then find the one with smallest distance to 'I love apples' embedding uses embeddings and distance, which is the correct method. Options A, C, and D do not use embeddings or meaningful similarity measures.

3. Final Answer:

   Compute embeddings for all sentences, then find the one with smallest distance to 'I love apples' embedding -> Option C

4. Quick Check:

   Use embeddings + distance for similarity [OK]

Hint: Use embedding distances to find similar texts [OK]

Common Mistakes:

• Using word count instead of embeddings
• Ignoring embeddings for similarity
• Random selection instead of comparison