Why embeddings capture semantic meaning in Prompt Engineering / GenAI - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an embedding vector for a word using a simple lookup.

Prompt Engineering / GenAI

embedding_vector = embedding_matrix[[1]]

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

Aword

Bword_index

Cembedding_matrix

Dvector

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word string directly instead of its index.

The embedding matrix uses the word's index to find its vector representation.

2fill in blank

medium

Complete the code to calculate cosine similarity between two embedding vectors.

Prompt Engineering / GenAI

similarity = (vec1 @ vec2) / ([1](vec1) * np.linalg.norm(vec2))

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

Anp.linalg.norm

Bnp.dot

Cnp.sum

Dnp.mean

Attempts:

3 left

💡 Hint

Common Mistakes

Using dot product instead of norm in the denominator.

Cosine similarity divides the dot product by the product of the vectors' norms.

3fill in blank

hard

Fix the error in the code that normalizes an embedding vector.

Prompt Engineering / GenAI

normalized_vec = vec / np.linalg.[1](vec)

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

Adot

Bmean

Csum

Dnorm

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum or mean instead of norm for normalization.

Normalization divides by the vector's norm to scale it to length 1.

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'

Aembedding_matrix[word_index[word]]

Bembedding_matrix[word]

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word string directly as index.

Using wrong comparison operator.

We get the embedding by indexing the matrix with the word's index, and filter words longer than 3 letters.

5fill in blank

hard

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

Prompt Engineering / GenAI

filtered_embeddings = { [1]: [2] for [3] in word_embeddings if np.linalg.norm(word_embeddings[[1]]) > 0.5 }

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

Aword

Bembedding

Dvector

Attempts:

3 left

💡 Hint

Common Mistakes

Using inconsistent variable names.

Using 'vector' instead of 'embedding'.

We use 'word' as key, 'embedding' as value, and iterate over 'word' in the dictionary.

Practice

1. Why do embeddings help computers understand language better?

easy

A. Because they store words as images

B. Because they turn words into numbers that show meaning

C. Because they translate words into different languages

D. Because they count how many letters are in a word

Solution

1. Step 1: Understand what embeddings do

   Embeddings convert words or ideas into numbers that capture their meaning.

2. Step 2: Recognize why this helps computers

   Numbers allow computers to compare and find similarities between words easily.

3. Final Answer:

   Because they turn words into numbers that show meaning -> Option B

4. Quick Check:

   Embeddings = numbers showing meaning [OK]

Hint: Embeddings = numbers that capture meaning [OK]

Common Mistakes:

• Thinking embeddings store images
• Confusing embeddings with translation
• Believing embeddings count letters

2. Which of the following is the correct way to say embeddings capture semantic meaning?

easy

A. Embeddings count the frequency of words

B. Embeddings store words as raw text strings

C. Embeddings translate words into pictures

D. Embeddings map words to vectors of numbers

Solution

1. Step 1: Identify the correct technical description

   Embeddings represent words as vectors (lists) of numbers.

2. Step 2: Eliminate incorrect options

   Raw text, pictures, and frequency counts do not capture semantic meaning as embeddings do.

3. Final Answer:

   Embeddings map words to vectors of numbers -> Option D

4. Quick Check:

   Embeddings = vectors of numbers [OK]

Hint: Embeddings = vectors, not raw text or images [OK]

Common Mistakes:

• Confusing embeddings with raw text storage
• Thinking embeddings are images
• Mixing embeddings with word counts

3. Given two embeddings: embedding1 = [0.1, 0.3, 0.5] and embedding2 = [0.1, 0.31, 0.49], what can we say about their semantic similarity?

medium

A. They have no relation in meaning

B. They are very different in meaning

C. They are somewhat similar in meaning

D. They are exactly the same meaning

Solution

1. Step 1: Compare the two embeddings numerically

   The numbers are close but not identical, showing some similarity.

2. Step 2: Understand what closeness means in embeddings

   Close embeddings mean similar meanings, but not exactly the same.

3. Final Answer:

   They are somewhat similar in meaning -> Option C

4. Quick Check:

   Close vectors = similar meaning [OK]

Hint: Close embeddings mean similar meaning [OK]

Common Mistakes:

• Assuming small differences mean no similarity
• Thinking embeddings must be identical to be similar
• Ignoring numerical closeness

4. Look at this code snippet that tries to find similarity between two embeddings:

embedding1 = [0.2, 0.4, 0.6]
embedding2 = [0.2, 0.4, 0.6]

similarity = sum(embedding1[i] * embedding2[i] for i in range(3))
print(similarity)

What is the error in this code?

medium

A. The code correctly computes dot product similarity

B. The code should normalize embeddings before dot product

C. The code uses sum incorrectly; it should use a loop

D. The code uses wrong indices for embeddings

Solution

1. Step 1: Analyze the code logic

   The code calculates the dot product by summing element-wise products.

2. Step 2: Check if this is a valid similarity measure

   Dot product is a common way to measure similarity between embeddings.

3. Final Answer:

   The code correctly computes dot product similarity -> Option A

4. Quick Check:

   Dot product code is correct [OK]

Hint: Dot product sums element-wise products [OK]

Common Mistakes:

• Thinking sum can't be used with generator expressions
• Believing normalization is always required
• Confusing indices usage

5. You have embeddings for words: 'cat', 'dog', and 'car'. Which embedding pair is expected to be closest in meaning and why?

hard

A. Embeddings of 'cat' and 'dog' because both are animals

B. Embeddings of 'cat' and 'car' because they start with the same letter

C. Embeddings of 'dog' and 'car' because they have the same number of letters

D. Embeddings of 'cat' and 'dog' because they rhyme

Solution

1. Step 1: Understand semantic meaning in embeddings

   Embeddings capture meaning, so similar concepts have closer embeddings.

2. Step 2: Compare the word pairs by meaning

   'Cat' and 'dog' are both animals, so their embeddings should be closer than unrelated words.

3. Final Answer:

   Embeddings of 'cat' and 'dog' because both are animals -> Option A

4. Quick Check:

   Similar meaning = closer embeddings [OK]

Hint: Semantic similarity beats spelling or sound [OK]

Common Mistakes:

• Choosing words based on spelling or sound
• Ignoring actual meaning of words
• Assuming letter count affects embeddings