Few-shot prompting in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to add examples to the prompt for few-shot learning.

Prompt Engineering / GenAI

prompt = "Translate English to French:\n" + [1] + "\nInput: How are you?\nOutput:"

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

A"English: Hello\nFrench: Hola"

B"English: Hello\nFrench: Bonjour"

C"English: Hello\nFrench: Ciao"

D"English: Hello\nFrench: Hallo"

Attempts:

3 left

💡 Hint

Common Mistakes

Using greetings in other languages like Spanish or Italian.

Not formatting the example as 'English: ...\nFrench: ...'.

The example must show English to French translation. 'Bonjour' is French for 'Hello'.

2fill in blank

medium

Complete the code to add multiple examples separated by newlines for few-shot prompting.

Prompt Engineering / GenAI

examples = "English: Hello\nFrench: Bonjour\n" + [1] + "\nInput: Thank you\nOutput:"

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

A"English: Goodbye\nFrench: Au revoir"

B"English: Goodbye\nFrench: Adios"

C"English: Goodbye\nFrench: Ciao"

D"English: Goodbye\nFrench: Hallo"

Attempts:

3 left

💡 Hint

Common Mistakes

Using Spanish or Italian words instead of French.

Not separating examples with newlines.

The second example must be a correct English to French pair. 'Au revoir' means 'Goodbye' in French.

3fill in blank

hard

Fix the error in the prompt construction to correctly format few-shot examples.

Prompt Engineering / GenAI

prompt = "Translate English to French:\n" + examples + [1] + "Input: Good night\nOutput:"

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

A""

B" "

C"\t"

D"\n"

Attempts:

3 left

💡 Hint

Common Mistakes

Using a space or tab instead of a newline.

Not adding any separator causing formatting issues.

A newline '\n' is needed to separate the examples from the new input prompt.

4fill in blank

hard

Fill both blanks to create a function that builds a few-shot prompt with given examples and input.

Prompt Engineering / GenAI

def build_prompt(examples, input_text):
    return "Translate English to French:\n" + [1] + "\nInput: " + [2] + "\nOutput:"

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

Aexamples

Binput_text

Cexamples + '\n'

D'input_text'

Attempts:

3 left

💡 Hint

Common Mistakes

Not adding a newline after examples.

Using a string literal 'input_text' instead of the variable.

We add a newline after examples (C) and use the input_text variable (B) for the input.

5fill in blank

hard

Fill all three blanks to complete the few-shot prompt generation with multiple examples and input.

Prompt Engineering / GenAI

examples = [
    "English: Hello\nFrench: Bonjour",
    "English: Goodbye\nFrench: Au revoir",
    "English: Thank you\nFrench: Merci"
]
prompt = "Translate English to French:\n" + [1] + "\nInput: " + [2] + "\nOutput:"

response = model.generate(prompt, max_tokens=[3])

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

A'\n'.join(examples)

Binput_sentence

C50

D100

Attempts:

3 left

💡 Hint

Common Mistakes

Using commas instead of newlines to join examples.

Using a string literal instead of the input variable.

Setting max_tokens too high or too low.

Join examples with newlines (A), use input_sentence variable (B), and set max_tokens to 50 (C) for output length.

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