Key models overview (GPT, DALL-E, Stable Diffusion) in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the primary function of GPT models?

GPT models are designed mainly to:

A Enhance audio signals for speech clarity

B Generate and understand human-like text

C Create detailed images from text descriptions

D Predict stock market trends using numerical data

💡 Hint

Think about what GPT stands for and what it is famous for.

GPT (Generative Pre-trained Transformer) models specialize in generating and understanding text, making them powerful for chatbots, writing, and language tasks.

❓ Predict Output

intermediate

Output of a Stable Diffusion image generation step

Given a text prompt, what type of output does Stable Diffusion produce?

A A sound clip related to the prompt

B A text summary describing the prompt

C A high-resolution image matching the prompt

D A 3D model file of the described object

💡 Hint

Stable Diffusion is known for creating visuals from words.

Stable Diffusion generates images from text prompts by gradually refining noise into a detailed picture.

❓ Model Choice

advanced

Choosing the right model for text-to-image generation

You want to create an artistic image from a text description. Which model is best suited?

A Stable Diffusion

B GPT

C DALL-E

D BERT

💡 Hint

Consider which models specialize in image creation from text.

Stable Diffusion is a popular open-source model for generating images from text prompts, known for artistic and detailed outputs.

❓ Metrics

advanced

Evaluating GPT model text generation quality

Which metric is commonly used to measure how well GPT models generate coherent and relevant text?

A Fréchet Inception Distance

B Inception Score

C Mean Squared Error

D BLEU score

💡 Hint

Think about metrics used in natural language processing.

BLEU score measures how closely generated text matches reference text, commonly used for language models like GPT.

🔧 Debug

expert

Why might a DALL-E model fail to generate an image from a prompt?

Given a valid text prompt, which of the following is the most likely cause for DALL-E not producing an image?

A The prompt contains unsupported special characters causing tokenization errors

B The model is trained only on audio data, so it cannot generate images

C The prompt is too short, so the model refuses to run

D The model requires GPU but is running on CPU, which is unsupported

💡 Hint

Consider how text inputs are processed before image generation.

DALL-E tokenizes text prompts; unsupported characters can cause tokenization errors preventing image generation.

Practice

1. Which model is mainly used to generate human-like text?

easy

A. GPT

B. DALL-E

C. Stable Diffusion

D. None of the above

Solution

1. Step 1: Understand GPT's purpose

   GPT is designed to generate and understand human-like text.

2. Step 2: Compare with other models

   DALL-E and Stable Diffusion create images, not text.

3. Final Answer:

   GPT -> Option A

4. Quick Check:

   Text generation = GPT [OK]

Hint: Text output? Think GPT first. [OK]

Common Mistakes:

• Confusing DALL-E as text model
• Thinking Stable Diffusion generates text
• Choosing 'None of the above'

2. Which of the following is the correct way to describe DALL-E's function?

easy

A. It generates text based on images.

B. It compresses images for storage.

C. It creates images from text descriptions.

D. It translates text from one language to another.

Solution

1. Step 1: Identify DALL-E's main function

   DALL-E creates images from text prompts given by users.

2. Step 2: Eliminate incorrect options

   It does not generate text, translate languages, or compress images.

3. Final Answer:

   It creates images from text descriptions. -> Option C

4. Quick Check:

   Text to image = DALL-E [OK]

Hint: DALL-E = text to image creator. [OK]

Common Mistakes:

• Thinking DALL-E generates text
• Confusing with translation models
• Assuming it compresses images

3. Given the following code snippet using a model, what type of output should you expect?

model = 'Stable Diffusion'
input_text = 'A sunny beach with palm trees'
output = model.generate(input_text)

medium

A. A photo-realistic image of a sunny beach

B. A summary of the text input

C. A written story about a beach

D. An error because Stable Diffusion cannot generate output

Solution

1. Step 1: Identify Stable Diffusion's output type

   Stable Diffusion generates images from text prompts.

2. Step 2: Match input and output

   Input is a text description; output will be an image matching that description.

3. Final Answer:

   A photo-realistic image of a sunny beach -> Option A

4. Quick Check:

   Text input + Stable Diffusion = Image output [OK]

Hint: Stable Diffusion turns words into pictures. [OK]

Common Mistakes:

• Expecting text output
• Thinking it summarizes text
• Assuming it causes an error

4. You tried to use GPT to create an image by running this code:

model = 'GPT'
input_text = 'A cat sitting on a sofa'
output = model.generate_image(input_text)

What is the main problem here?

medium

A. The input text is too short for GPT to understand.

B. GPT cannot generate images; it only generates text.

C. The method name should be generate_text, not generate_image.

D. There is no problem; the code will work fine.

Solution

1. Step 1: Understand GPT's capabilities

   GPT is designed to generate text, not images.

2. Step 2: Analyze the method call

   Calling generate_image on GPT is invalid because GPT lacks image generation ability.

3. Final Answer:

   GPT cannot generate images; it only generates text. -> Option B

4. Quick Check:

   GPT = text only, no images [OK]

Hint: GPT does text, not images. [OK]

Common Mistakes:

• Thinking GPT can create images
• Believing method name is wrong only
• Ignoring model capability limits

5. You want to build an app that lets users type a prompt to generate a story and then see an image illustrating it. Which combination of models should you use?

hard

A. Use GPT for image generation and DALL-E for text generation.

B. Use DALL-E to generate the story and GPT to create the image.

C. Use Stable Diffusion for both story and image generation.

D. Use GPT to generate the story and Stable Diffusion to create the image.

Solution

1. Step 1: Identify model roles for text and image

   GPT is best for generating human-like text stories.

2. Step 2: Identify model for image creation

   Stable Diffusion creates images from text descriptions, perfect for illustrating stories.

3. Final Answer:

   Use GPT to generate the story and Stable Diffusion to create the image. -> Option D

4. Quick Check:

   Text by GPT + Image by Stable Diffusion = App [OK]

Hint: Text with GPT, images with Stable Diffusion. [OK]

Common Mistakes:

• Swapping roles of GPT and DALL-E
• Using one model for both tasks
• Confusing image and text generation roles