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

Challenge - 5 Problems

🧠 Conceptual

intermediate

Choosing between fine-tuning and prompt engineering

You have a general language model and want it to perform well on a specific task with limited data. Which approach is best to start with?

A Fine-tune the model immediately using the limited data to specialize it.

B Ignore the task and use the model as is without any changes.

C Train a new model from scratch on the limited data.

D Use prompt engineering to guide the model without changing its weights.

💡 Hint

Think about the cost and data needed for fine-tuning versus prompt engineering.

Prompt engineering is usually the first step when data is limited because it does not require changing the model weights and is faster and cheaper. Fine-tuning is better when you have enough data and need more specialized behavior.

🧠 Conceptual

intermediate

When is fine-tuning preferred over prompt engineering?

Which situation best justifies fine-tuning a large language model instead of relying on prompt engineering?

A You want to use the model for many different unrelated tasks.

B You have a large, high-quality dataset specific to your task and need consistent output.

C You want to save computational resources and avoid retraining.

D You want to quickly test different instructions without changing the model.

💡 Hint

Consider when changing the model weights is beneficial.

Fine-tuning is preferred when you have enough task-specific data and want the model to consistently perform well on that task, as it adjusts the model weights to specialize it.

❓ Metrics

advanced

Evaluating fine-tuning vs prompt engineering performance

You fine-tune a model and also try prompt engineering on the base model. You measure accuracy on a test set. Which metric result indicates fine-tuning improved performance?

Prompt Engineering / GenAI

base_accuracy = 0.75
fine_tuned_accuracy = 0.82
prompt_engineered_accuracy = 0.78

A Fine-tuning improved accuracy by 7% over base and 4% over prompt engineering.

B Prompt engineering improved accuracy more than fine-tuning.

C Base model accuracy is highest, so no improvement.

D Fine-tuning decreased accuracy compared to prompt engineering.

💡 Hint

Compare the accuracy numbers carefully.

Fine-tuning accuracy (0.82) is higher than both base (0.75) and prompt engineered (0.78), showing it improved performance the most.

🔧 Debug

advanced

Why did fine-tuning not improve model performance?

You fine-tuned a model on a small dataset but test accuracy dropped. What is the most likely cause?

A Prompt engineering was not used before fine-tuning.

B The model architecture was changed accidentally.

C The dataset was too small causing overfitting during fine-tuning.

D The test set was too large compared to training.

💡 Hint

Think about what happens when fine-tuning with little data.

Fine-tuning on a small dataset can cause the model to memorize training data and perform worse on new data, known as overfitting.

❓ Model Choice

expert

Selecting approach for a multi-domain chatbot

You want to build a chatbot that handles many topics without retraining often. Which approach is best?

A Use prompt engineering with a single large base model to adapt responses dynamically.

B Fine-tune separate models for each domain and switch between them.

C Train a new model from scratch on combined domain data.

D Use a rule-based system instead of a language model.

💡 Hint

Consider flexibility and maintenance effort for many topics.

Prompt engineering with a single large model allows dynamic adaptation to many domains without retraining multiple models, making it more scalable and efficient.

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