Text embedding models in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main purpose of text embedding models?

Text embedding models convert text into numbers. What is their main purpose?

A To transform text into fixed-length vectors that capture semantic meaning

B To translate text from one language to another

C To generate new text based on a prompt

D To count the number of words in a sentence

💡 Hint

Think about how computers understand text in a way that machines can work with.

Text embedding models create fixed-length vectors that represent the meaning of text, enabling machines to compare and analyze text effectively.

❓ Model Choice

intermediate

Which model is best suited for generating text embeddings?

You want to create embeddings for sentences to compare their meanings. Which model type is best?

A Transformer-based model trained on large text corpora

B Recurrent Neural Network (RNN) trained on time series data

C Convolutional Neural Network (CNN) trained on images

D Generative Adversarial Network (GAN) for image generation

💡 Hint

Consider models designed to understand language context deeply.

Transformer-based models like BERT or GPT are trained on large text datasets and excel at capturing semantic relationships in text, making them ideal for embeddings.

❓ Predict Output

advanced

What is the output shape of embeddings for 3 sentences using a model that outputs 768-dimensional vectors?

Given this Python code snippet:

sentences = ["Hello world", "Machine learning is fun", "AI helps humans"]
embeddings = model.encode(sentences)
print(embeddings.shape)

What will be printed?

Prompt Engineering / GenAI

sentences = ["Hello world", "Machine learning is fun", "AI helps humans"]
embeddings = model.encode(sentences)
print(embeddings.shape)

A (768, 3)

B (3, 768)

C (3,)

D (768,)

💡 Hint

Each sentence gets a vector of length 768. How many sentences are there?

The model encodes each sentence into a 768-dimensional vector. Since there are 3 sentences, the output shape is (3, 768).

❓ Metrics

advanced

Which metric is best to measure similarity between two text embeddings?

You have two text embeddings and want to measure how similar their meanings are. Which metric is most appropriate?

A Euclidean distance

B Accuracy

C Mean squared error

D Cosine similarity

💡 Hint

Think about a metric that measures the angle between two vectors rather than their length.

Cosine similarity measures the angle between two vectors, focusing on their direction, which is ideal for comparing semantic similarity of embeddings.

🔧 Debug

expert

Why does this embedding code raise a TypeError?

Consider this code snippet:

text = "AI is amazing"
embedding = model.encode(text)
print(embedding.shape)

It raises: TypeError: 'float' object has no attribute 'shape'. Why?

A The variable 'text' is not defined

B The model.encode returns a float instead of a vector

C The model.encode expects a list of strings, not a single string

D The print statement is missing parentheses

💡 Hint

Check the input type expected by the encode method.

Many embedding models expect a list of strings to encode multiple texts. Passing a single string returns a vector, but sometimes it returns a float if the model is misused. Here, the error suggests the input should be a list like [text].

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