Few-shot prompting in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Few-shot Prompting Basics

What is the main advantage of few-shot prompting when using large language models?

A It provides a few examples in the prompt to guide the model's output.

B It allows the model to learn new tasks without any examples.

C It requires retraining the model with a large dataset.

D It reduces the model size to improve speed.

💡 Hint

Think about how few-shot prompting helps the model understand what you want by showing examples.

Few-shot prompting works by giving the model a few examples in the prompt so it can mimic the pattern and produce the desired output.

❓ Predict Output

intermediate

Predicting Output from Few-shot Prompt

Given this prompt to a language model, what is the most likely output? Prompt: "Translate English to French: 1. Hello -> Bonjour 2. Thank you -> Merci 3. Good night ->"

A Bonne nuit

B Bonsoir

C Salut

D Au revoir

💡 Hint

Look at the pattern of English phrases and their French translations.

The prompt shows English phrases with their French translations. 'Good night' translates to 'Bonne nuit' in French.

❓ Model Choice

advanced

Choosing the Best Model for Few-shot Prompting

Which type of model is best suited for few-shot prompting tasks?

A Small models trained only on specific tasks

B Large pretrained language models with broad knowledge

C Models trained only on numerical data

D Models without any pretraining

💡 Hint

Few-shot prompting relies on the model's prior knowledge and ability to generalize.

Large pretrained language models have learned broad patterns and knowledge, making them effective for few-shot prompting.

❓ Hyperparameter

advanced

Effect of Temperature in Few-shot Prompting

In few-shot prompting, what effect does increasing the temperature parameter have on the model's output?

A Decreases the length of the output

B Makes the output more deterministic and repetitive

C Makes the output more random and creative

D Stops the model from generating any output

💡 Hint

Temperature controls randomness in the model's choices.

Higher temperature values increase randomness, making outputs more diverse and creative.

❓ Metrics

expert

Evaluating Few-shot Prompting Performance

Which metric is most appropriate to evaluate the quality of few-shot prompting outputs for a text classification task?

A BLEU score

B Mean Squared Error (MSE)

C Perplexity

D Accuracy

💡 Hint

Think about the task type and what metric measures correct predictions.

Accuracy measures how many predictions match the true labels, suitable for classification tasks.

Practice

1. What is the main idea behind few-shot prompting in AI models?

easy

A. Showing a few examples in the prompt to teach the model a task

B. Training the model with a large dataset from scratch

C. Using no examples and relying on random guesses

D. Fine-tuning the model with many epochs

Solution

1. Step 1: Understand few-shot prompting concept

   Few-shot prompting means giving the model a few examples in the prompt to help it understand the task.

2. Step 2: Compare with other methods

   Unlike training or fine-tuning, few-shot prompting does not require changing the model weights, just examples in the prompt.

3. Final Answer:

   Showing a few examples in the prompt to teach the model a task -> Option A

4. Quick Check:

   Few-shot prompting = examples in prompt [OK]

Hint: Few-shot means few examples shown in prompt [OK]

Common Mistakes:

• Confusing few-shot prompting with full model training
• Thinking it requires many examples
• Assuming no examples are given

2. Which of the following is the correct way to include examples in a few-shot prompt?

easy

A. Add random unrelated text before the question

B. Write only the new question without examples

C. List examples clearly, then ask the new question

D. Use code comments instead of examples

Solution

1. Step 1: Identify proper prompt structure

   Few-shot prompting works best when examples are clearly listed before the new question.

2. Step 2: Eliminate incorrect options

   Options A, B, and D do not provide clear examples or add unrelated content, which confuses the model.

3. Final Answer:

   List examples clearly, then ask the new question -> Option C

4. Quick Check:

   Clear examples first = correct prompt [OK]

Hint: Put examples before the question in prompt [OK]

Common Mistakes:

• Skipping examples completely
• Adding unrelated text that confuses the model
• Using comments instead of examples

3. Given this few-shot prompt for a model:

Q: What is 2 + 3?
A: 5
Q: What is 4 + 1?
A: 5
Q: What is 7 + 2?
A:

What will the model most likely answer?

medium

A. 5

B. 9

C. 7

D. 2

Solution

1. Step 1: Analyze the examples given

   The examples show addition questions with correct answers: 2+3=5 and 4+1=5.

2. Step 2: Predict the answer for 7 + 2

   7 + 2 equals 9, so the model should answer 9 following the pattern.

3. Final Answer:

   9 -> Option B

4. Quick Check:

   7+2=9 [OK]

Hint: Add numbers as shown in examples [OK]

Common Mistakes:

• Repeating previous answer 5
• Confusing question numbers
• Ignoring addition operation

4. You wrote this few-shot prompt:

Q: Translate 'cat' to Spanish.
A: gato
Q: Translate 'dog' to Spanish.
A: perro
Q: Translate 'bird' to Spanish.
A: perro

What is the main error here?

medium

A. The last answer repeats 'perro' instead of 'pájaro'

B. The examples are unrelated to translation

C. The prompt is missing the question marks

D. The answers are in English, not Spanish

Solution

1. Step 1: Check the last example's answer

   The last question asks for 'bird' in Spanish, but the answer repeats 'perro' (dog).

2. Step 2: Identify correct Spanish word

   The correct Spanish word for 'bird' is 'pájaro', so the answer is wrong.

3. Final Answer:

   The last answer repeats 'perro' instead of 'pájaro' -> Option A

4. Quick Check:

   Wrong repeated answer = error [OK]

Hint: Check if answers match questions correctly [OK]

Common Mistakes:

• Copying previous answer by mistake
• Ignoring answer correctness
• Assuming question marks are required

5. You want to create a few-shot prompt to help a model classify fruits as 'sweet' or 'sour'. Which prompt is best?

hard

A. Q: What color is lemon?\nA: yellow\nQ: What color is apple?\nA: red\nQ: What color is orange?\nA:

B. Q: Is lemon sweet or sour?\nA: sweet\nQ: Is apple sweet or sour?\nA: sour\nQ: Is orange sweet or sour?\nA:

C. Q: Is lemon a fruit?\nA: yes\nQ: Is apple a fruit?\nA: yes\nQ: Is orange a fruit?\nA:

D. Q: Is lemon sweet or sour?\nA: sour\nQ: Is apple sweet or sour?\nA: sweet\nQ: Is orange sweet or sour?\nA:

Solution

1. Step 1: Identify the task in the prompt

   The task is to classify fruits as 'sweet' or 'sour', so examples must show this classification clearly.

2. Step 2: Evaluate each option's relevance

   Q: Is lemon sweet or sour?\nA: sour\nQ: Is apple sweet or sour?\nA: sweet\nQ: Is orange sweet or sour?\nA: correctly shows examples of fruits labeled 'sweet' or 'sour'. Options B, C, and D either reverse labels or ask unrelated questions.

3. Final Answer:

   Q: Is lemon sweet or sour? A: sour Q: Is apple sweet or sour? A: sweet Q: Is orange sweet or sour? A: -> Option D

4. Quick Check:

   Examples match task = best prompt [OK]

Hint: Match examples to the exact task asked [OK]

Common Mistakes:

• Mixing up labels in examples
• Using unrelated questions
• Not showing clear classification