Few-shot prompting in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is few-shot prompting in AI?

Few-shot prompting is a way to teach an AI model by giving it a few examples of the task you want it to do, so it can learn and perform the task without needing a lot of training.

beginner

Why is few-shot prompting useful?

It helps AI models understand new tasks quickly with only a small number of examples, saving time and resources compared to training from scratch.

intermediate

How does few-shot prompting differ from zero-shot prompting?

Few-shot prompting gives the AI some examples to learn from, while zero-shot prompting asks the AI to perform a task without any examples.

beginner

Give an example of few-shot prompting for a text classification task.

Example: Provide 3 sentences labeled as 'positive' or 'negative' sentiment, then ask the AI to classify a new sentence based on those examples.

intermediate

What is a key challenge when using few-shot prompting?

Choosing clear and representative examples is important because poor examples can confuse the AI and reduce its performance.

What does few-shot prompting provide to an AI model?

A A few examples of the task

B No examples at all

C Thousands of training samples

D Only the task description

Few-shot prompting gives the AI a small number of examples to learn from.

Which is true about zero-shot prompting compared to few-shot prompting?

A Zero-shot uses many examples

B Zero-shot uses no examples

C Zero-shot uses fewer examples than few-shot

D Zero-shot is the same as few-shot

Zero-shot prompting asks the AI to perform a task without any examples.

Why might few-shot prompting be preferred over full training?

A It needs less data and time

B It uses complex algorithms

C It always gives perfect results

D It requires no examples

Few-shot prompting saves time and data by using only a few examples.

What is important when selecting examples for few-shot prompting?

A Examples should be confusing

B Examples should be unrelated

C Examples should be clear and representative

D Examples should be very long

Clear and representative examples help the AI learn the task better.

Which task can benefit from few-shot prompting?

A Formatting a document

B Running a web server

C Compiling code

D Classifying emails as spam or not

Few-shot prompting is useful for tasks like text classification.

Explain what few-shot prompting is and why it is useful in AI.

Think about teaching a friend with just a few examples.

Describe the difference between few-shot and zero-shot prompting.

Consider how many examples the AI sees before doing the task.

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