Content writing assistance in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a pre-trained language model for content writing assistance.

Prompt Engineering / GenAI

from transformers import [1]
model = [1].from_pretrained('gpt2')

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

AGPT2Model

BGPT2Tokenizer

CBertModel

DGPT2LMHeadModel

Attempts:

3 left

💡 Hint

Common Mistakes

Using GPT2Tokenizer instead of GPT2LMHeadModel

Using BertModel which is a different architecture

We use GPT2LMHeadModel to load the GPT-2 model with language modeling head for text generation.

2fill in blank

medium

Complete the code to tokenize input text for the model.

Prompt Engineering / GenAI

from transformers import GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
inputs = tokenizer('[1]', return_tensors='pt')

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AHello, how are you?

Btokenize this text

CGenerate content

DSample input

Attempts:

3 left

💡 Hint

Common Mistakes

Passing variable names instead of string literals

Using empty strings or non-text inputs

We provide a sample input text like 'Hello, how are you?' to tokenize for the model.

3fill in blank

hard

Fix the error in the code to generate text from the model.

Prompt Engineering / GenAI

outputs = model.generate([1]['input_ids'], max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Ainputs

Binput

Ctokens

Dtext

Attempts:

3 left

💡 Hint

Common Mistakes

Using undefined variable names like 'input' or 'tokens'

Passing raw text instead of token ids

The tokenized inputs are stored in 'inputs', so we use inputs['input_ids'] to generate text.

4fill in blank

hard

Fill both blanks to create a function that generates content given a prompt.

Prompt Engineering / GenAI

def generate_content(prompt):
    inputs = tokenizer([1], return_tensors='pt')
    outputs = model.generate(inputs[[2]], max_length=100)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

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Aprompt

Binput_ids

Cattention_mask

Dtext

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'text' instead of 'prompt' as input

Using 'attention_mask' for generation input

We pass the prompt string to tokenizer and use 'input_ids' key for generation.

5fill in blank

hard

Fill all three blanks to create a pipeline for content writing assistance.

Prompt Engineering / GenAI

from transformers import pipeline
content_generator = pipeline('[1]', model='gpt2')
result = content_generator('[2]', max_length=[3])
print(result[0]['generated_text'])

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

Atext-generation

BWrite a short story

C50

Dtext-classification

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'text-classification' instead of 'text-generation'

Passing numeric prompt instead of string

Setting max_length too low or missing

We use 'text-generation' pipeline with a prompt and max_length for output length.

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