When to fine-tune vs prompt engineer in Prompt Engineering / GenAI - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a prompt that asks the model to summarize text.

Prompt Engineering / GenAI

prompt = "Summarize the following text: [1]"

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

Atokenizer

Binput_text

Coutput

Dmodel

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'model' or 'tokenizer' instead of the actual text input.

Leaving the prompt incomplete without the text.

The prompt should include the input text to be summarized.

2fill in blank

medium

Complete the code to load a pre-trained model for fine-tuning.

Prompt Engineering / GenAI

model = AutoModelForSequenceClassification.from_pretrained([1])

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

A"output"

B"tokenizer"

C"input_text"

D"bert-base-uncased"

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'tokenizer' or other unrelated strings.

Not providing any model name.

The model name or path is needed to load the pre-trained model.

3fill in blank

hard

Fix the error in the code to fine-tune the model on new data.

Prompt Engineering / GenAI

optimizer = AdamW(model.parameters(), lr=[1])

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

A"0.001"

B5

C0.001

D0.01

Attempts:

3 left

💡 Hint

Common Mistakes

Using integer values like 5.

Passing learning rate as a string.

Learning rate should be a small float like 0.001, not an integer or string.

4fill in blank

hard

Fill both blanks to create a prompt template and insert the user input.

Prompt Engineering / GenAI

template = "Answer the question: [1]"
prompt = template.format([2]=user_question)

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

A{}

Bquestion

Cuser_question

Dinput

Attempts:

3 left

💡 Hint

Common Mistakes

Using '{}' for placeholders but trying to use named format keys.

Using wrong placeholder syntax.

The template uses named placeholders and the format keyword should match the placeholder name.

5fill in blank

hard

Fill all three blanks to prepare data for fine-tuning with labels.

Prompt Engineering / GenAI

dataset = [{'[1]': text, '[2]': label} for text, label in zip([3], labels)]

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

Atext

Blabel

Ctexts

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong keys like 'data' or 'labels' as keys.

Using 'data' instead of 'texts' for input list.

The dictionary keys should be 'text' and 'label', and the list of texts is 'texts'.

Practice

1. What is the main difference between fine-tuning a model and prompt engineering?

easy

A. Fine-tuning is faster than prompt engineering.

B. Fine-tuning changes the prompt format, while prompt engineering changes the model's weights.

C. Fine-tuning changes the model's knowledge, while prompt engineering changes how you ask questions.

D. Prompt engineering requires retraining the model.

Solution

1. Step 1: Understand fine-tuning

   Fine-tuning means adjusting the model's internal settings (weights) to better fit specific data or tasks.

2. Step 2: Understand prompt engineering

   Prompt engineering means changing the way you ask the model questions without changing the model itself.

3. Final Answer:

   Fine-tuning changes the model's knowledge, while prompt engineering changes how you ask questions. -> Option C

4. Quick Check:

   Fine-tune = model change, prompt engineer = question change [OK]

Hint: Fine-tune = model change; prompt engineer = question change [OK]

Common Mistakes:

• Confusing prompt engineering with model retraining
• Thinking fine-tuning only changes prompts
• Believing prompt engineering is slower than fine-tuning

2. Which of the following is a correct way to describe prompt engineering?

easy

A. Adjusting the input text to get better model responses.

B. Changing the model's training data to improve accuracy.

C. Rebuilding the model architecture from scratch.

D. Adding new layers to the model for customization.

Solution

1. Step 1: Identify prompt engineering meaning

   Prompt engineering means changing how you write or format the input text to guide the model's answers.

2. Step 2: Check options

   Only Adjusting the input text to get better model responses. describes adjusting input text, which matches prompt engineering.

3. Final Answer:

   Adjusting the input text to get better model responses. -> Option A

4. Quick Check:

   Prompt engineering = input text change [OK]

Hint: Prompt engineering means changing input text, not model structure [OK]

Common Mistakes:

• Mixing prompt engineering with model retraining
• Thinking prompt engineering changes model layers
• Confusing prompt engineering with data augmentation

3. Consider you want to improve a model's answers for a very specific medical dataset. Which approach will likely give better results?

medium

A. Only use prompt engineering to rewrite questions.

B. Use random prompts without changes.

C. Ignore the dataset and use the base model.

D. Fine-tune the model with the medical dataset.

Solution

1. Step 1: Understand the task

   Improving answers for a specific medical dataset requires the model to learn new, specialized knowledge.

2. Step 2: Choose the best method

   Fine-tuning the model with the medical data updates its knowledge, making it better for this task.

3. Final Answer:

   Fine-tune the model with the medical dataset. -> Option D

4. Quick Check:

   Specific data needs fine-tuning [OK]

Hint: Use fine-tuning for specialized data, not just prompt changes [OK]

Common Mistakes:

• Thinking prompt engineering alone fixes specialized knowledge
• Ignoring fine-tuning for domain-specific tasks
• Assuming base model works best without adaptation

4. You tried prompt engineering but the model still gives poor answers for your task. What is the most likely fix?

medium

A. Fine-tune the model with relevant data.

B. Change the model architecture.

C. Use shorter prompts.

D. Restart the model server.

Solution

1. Step 1: Analyze the problem

   If prompt engineering fails to improve answers, the model likely lacks task-specific knowledge.

2. Step 2: Choose the fix

   Fine-tuning with relevant data updates the model's knowledge to improve answers.

3. Final Answer:

   Fine-tune the model with relevant data. -> Option A

4. Quick Check:

   Poor answers + prompt fail = fine-tune needed [OK]

Hint: If prompts fail, fine-tune with data [OK]

Common Mistakes:

• Trying unrelated fixes like changing architecture
• Assuming shorter prompts fix knowledge gaps
• Restarting server won't improve model knowledge

5. You have a chatbot that answers general questions well but struggles with your company's product details. You want to improve it quickly without retraining. What should you do?

hard

A. Ignore product details and focus on general answers.

B. Use prompt engineering to add product info in the questions.

C. Replace the chatbot with a new model.

D. Fine-tune the entire model with product manuals.

Solution

1. Step 1: Identify constraints

   You want a quick improvement without retraining the model.

2. Step 2: Choose the best approach

   Prompt engineering lets you add product info in questions to guide the model without retraining.

3. Final Answer:

   Use prompt engineering to add product info in the questions. -> Option B

4. Quick Check:

   Quick fix without retrain = prompt engineering [OK]

Hint: Quick fix without retrain? Use prompt engineering [OK]

Common Mistakes:

• Thinking fine-tuning is always fastest
• Replacing model unnecessarily
• Ignoring product info causes poor answers