Text embedding models in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a text embedding using a simple model.

Prompt Engineering / GenAI

embedding = model.[1](text)

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

Atransform

Bembed

Cfit

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'predict' instead of 'transform' causes errors because embeddings are not predictions.

Using 'fit' tries to train the model, not create embeddings.

The transform method is used to convert text into embeddings in many models.

2fill in blank

medium

Complete the code to normalize the embedding vector.

Prompt Engineering / GenAI

normalized_embedding = embedding / [1](embedding)

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

Anp.linalg.norm

Blen

Csum

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sum' adds all elements but does not give vector length.

Using 'len' returns number of elements, not vector magnitude.

Using np.linalg.norm calculates the length of the vector for normalization.

3fill in blank

hard

Fix the error in the code to compute cosine similarity between two embeddings.

Prompt Engineering / GenAI

similarity = np.dot(embedding1, embedding2) / ([1](embedding1) * np.linalg.norm(embedding2))

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

Amax

Bsum

Clen

Dnp.linalg.norm

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sum' or 'len' causes incorrect similarity calculation.

Forgetting to normalize both vectors leads to wrong results.

Both vectors must be normalized by their norms to compute cosine similarity correctly.

4fill in blank

hard

Fill both blanks to create a dictionary of word embeddings for words longer than 3 letters.

Prompt Engineering / GenAI

word_embeddings = {word: [1] for word in words if len(word) [2] 3}

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

Amodel.transform(word)

Bmodel.fit(word)

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fit' instead of 'transform' tries to train the model on single words.

Using '<' filters words shorter than 3 letters, which is incorrect here.

We transform each word to get its embedding and filter words longer than 3 letters using >.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of embeddings where embedding norm is greater than 0.5.

Prompt Engineering / GenAI

filtered_embeddings = {word: emb for word, emb in embeddings.items() if [1](emb) [2] 0.5 and len(word) [3] 4}

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

Anp.linalg.norm

B>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' changes the filter logic incorrectly.

Not using norm function causes errors in filtering.

We use np.linalg.norm to get embedding length, filter embeddings with norm greater than 0.5, and words longer than 4 letters.

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