Content writing assistance in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

How does a language model generate text?

Imagine a language model trained to write stories. How does it decide the next word to write?

A It predicts the next word based on the previous words it has generated.

B It randomly picks any word from its vocabulary without context.

C It always repeats the first word it learned during training.

D It selects the longest word available in the vocabulary.

💡 Hint

Think about how sentences flow naturally when you speak or write.

Language models generate text by predicting the next word based on the words that came before, using patterns learned from training data.

❓ Predict Output

intermediate

Output of simple text generation code

What is the output of this Python code simulating a simple text generation step?

Prompt Engineering / GenAI

import random
words = ['hello', 'world', 'machine', 'learning']
context = ['machine']
next_word = random.choice(words)
print(next_word)

A Raises a TypeError

B Prints 'hello world' concatenated

C Always prints 'machine'

D Prints a random word from the list including 'machine'

💡 Hint

Look at how random.choice works on a list.

random.choice picks one element randomly from the list, so output can be any word from the list.

❓ Model Choice

advanced

Choosing a model for content writing assistance

You want to build a content writing assistant that can generate long, coherent articles. Which model type is best?

A A large transformer-based language model trained on diverse text data

B A clustering model grouping similar sentences

C A simple linear regression model predicting word counts

D A small decision tree model trained on article titles

💡 Hint

Think about models that understand language context deeply.

Transformer-based language models are designed to generate coherent and context-aware text, suitable for long articles.

❓ Metrics

advanced

Evaluating content writing model quality

Which metric is most appropriate to evaluate how well a content writing model generates fluent and relevant text?

A Accuracy of classifying text sentiment

B Mean Squared Error (MSE)

C BLEU score comparing generated text to reference text

D Number of words generated per second

💡 Hint

Think about metrics that compare generated text to human-written text.

BLEU score measures how similar generated text is to reference text, useful for evaluating text generation quality.

🔧 Debug

expert

Debugging unexpected repetitive output in text generation

A content writing model keeps repeating the same phrase over and over. What is the most likely cause?

A The optimizer learning rate is too high causing slow learning.

B The model's temperature parameter is set too low, causing low randomness.

C The model has too many layers causing overfitting.

D The training data was shuffled randomly before training.

💡 Hint

Consider how randomness affects text diversity.

A low temperature makes the model pick the highest probability word repeatedly, causing repetitive output.

Practice

1. What is the main purpose of content writing assistance using AI?

easy

A. To replace human writers completely

B. To only check spelling mistakes

C. To help create and improve text like emails and articles

D. To generate images for articles

Solution

1. Step 1: Understand content writing assistance

   Content writing assistance uses AI to help users write better text by suggesting improvements and generating content.

2. Step 2: Identify the main purpose

   The main goal is to assist in creating and improving text such as emails, articles, and summaries, not to replace humans or only fix spelling.

3. Final Answer:

   To help create and improve text like emails and articles -> Option C

4. Quick Check:

   Content writing assistance = help create and improve text [OK]

Hint: Focus on AI helping text, not replacing humans [OK]

Common Mistakes:

• Thinking AI replaces all human writers
• Believing it only fixes spelling
• Confusing text help with image generation

2. Which of the following is the correct way to call an AI model for content writing assistance in Python?

easy

A. response = ai_model.generate_text(prompt='Write an email')

B. response = ai_model.generateText(prompt='Write an email')

C. response = ai_model.generate-text(prompt='Write an email')

D. response = ai_model.generate text(prompt='Write an email')

Solution

1. Step 1: Check method naming conventions in Python

   Python methods use underscores and lowercase letters, so generate_text is correct.

2. Step 2: Identify syntax errors in other options

   generateText uses camelCase (not typical in Python), generate-text and generate text have invalid characters or spaces.

3. Final Answer:

   response = ai_model.generate_text(prompt='Write an email') -> Option A

4. Quick Check:

   Python method syntax = generate_text [OK]

Hint: Python methods use underscores, no spaces or hyphens [OK]

Common Mistakes:

• Using camelCase instead of snake_case
• Including spaces or hyphens in method names
• Misplacing parentheses or quotes

3. What will be the output of this Python code snippet using a content writing AI model?

prompt = 'Summarize the benefits of AI'
response = ai_model.generate_text(prompt=prompt)
print(response)

medium

A. Empty output with no text

B. An error because prompt is not defined

C. The exact prompt string printed

D. A summary text explaining AI benefits

Solution

1. Step 1: Understand the code flow

   The code sends a prompt to the AI model to generate text summarizing AI benefits.

2. Step 2: Predict the output

   The print statement outputs the AI-generated summary text, not the prompt or an error.

3. Final Answer:

   A summary text explaining AI benefits -> Option D

4. Quick Check:

   AI model generates summary text = output [OK]

Hint: AI generates text from prompt, not just echoing it [OK]

Common Mistakes:

• Thinking prompt variable is undefined
• Expecting the prompt string printed
• Assuming no output is returned

4. Identify the error in this code snippet for content writing assistance:

response = ai_model.generate_text(prompt='Write a summary')
print(response.text)

medium

A. The attribute 'text' does not exist on response

B. The prompt string is missing

C. The method generate_text is misspelled

D. print() function is used incorrectly

Solution

1. Step 1: Check the response object structure

   Usually, the response from generate_text is a string, not an object with a 'text' attribute.

2. Step 2: Identify the error cause

   Accessing response.text causes an error because response is already the text output.

3. Final Answer:

   The attribute 'text' does not exist on response -> Option A

4. Quick Check:

   response is string, no .text attribute [OK]

Hint: Check if response is string before using .text [OK]

Common Mistakes:

• Assuming response is an object with attributes
• Misspelling method names
• Misusing print function syntax

5. You want to use AI content writing assistance to generate a polite email reply that includes a summary of the original message. Which approach combines content generation and summarization correctly?

hard

A. Generate the polite reply directly without summarizing the original message

B. First generate a summary of the original message, then use it as context to generate the polite reply

C. Summarize the polite reply after generating it

D. Generate a summary and a reply separately without linking them

Solution

1. Step 1: Understand the task requirements

   You need a polite reply that includes a summary of the original message, so summarization must happen first.

2. Step 2: Combine summarization and generation logically

   Summarize the original message, then feed that summary as context to generate a polite reply that includes it.

3. Final Answer:

   First generate a summary of the original message, then use it as context to generate the polite reply -> Option B

4. Quick Check:

   Summarize first, then generate reply [OK]

Hint: Summarize original first, then generate reply using summary [OK]

Common Mistakes:

• Generating reply without summary context
• Summarizing reply instead of original message
• Treating summary and reply as unrelated