Module 1 Quiz - Prompt University

Time Estimate: 30-45 minutes
Passing Score: 80% (12/15 questions)

Assessment Overview

This assessment tests your understanding of the foundational concepts covered in Module 1:

The prompt-response paradigm
Prompt structure and components
In-context learning techniques
Core prompting principles

Take your time and refer back to the lessons if needed. This is a learning tool, not just a test!

Part 1: Multiple Choice Questions (15 questions)

Question 1: Prompt-Response Paradigm

What does it mean when we say LLMs maximize Pr(y|x)?

Your Answer
Correct Answer

Select the best answer:A) The model memorizes training data and retrieves exact matches
B) The model calculates probability of outputs given input and selects the most likely
C) The model randomly generates text based on keywords
D) The model uses a database to look up pre-written responses

Question 2: In-Context Learning

Which statement about in-context learning is TRUE?

Your Answer
Correct Answer

A) It requires updating the model’s parameters
B) It only works with very large language models
C) It enables task adaptation through examples in the prompt
D) It’s slower than traditional fine-tuning

Question 3: Prompt Components

Which of the following is NOT one of the four core prompt components?

Your Answer
Correct Answer

A) Instruction
B) Context
C) Temperature
D) Output Indicator

Question 4: Zero-Shot vs Few-Shot

When should you prefer zero-shot prompting over few-shot?

Your Answer
Correct Answer

A) For complex, ambiguous tasks
B) For well-defined tasks with strong models
C) When you need maximum accuracy
D) When working with small language models

Question 5: Reasoning Guidance

What is the primary benefit of adding “Let’s think step-by-step” to prompts?

Your Answer
Correct Answer

A) It makes the model run faster
B) It activates deeper reasoning and improves accuracy
C) It reduces the length of responses
D) It prevents the model from using training data

Question 6: Role Assignment

Why does role assignment (e.g., “You are a poet”) improve prompt effectiveness?

Your Answer
Correct Answer

A) It changes the model’s training data
B) It activates relevant patterns from the model’s training
C) It increases the model’s creativity
D) It makes the model more confident

Question 7: Context Provision

What is the main purpose of providing context in prompts?

Your Answer
Correct Answer

A) To make prompts longer and more impressive
B) To ground responses in facts and reduce hallucination
C) To confuse the model with extra information
D) To test the model’s reading comprehension

Question 8: Format and Delimiters

Why are delimiters (like triple quotes) important in prompts?

Your Answer
Correct Answer

A) They make prompts look more professional
B) They clearly separate different parts of the prompt
C) They are required by all LLMs
D) They increase processing speed

Question 9: Few-Shot Learning

How many examples typically constitute “few-shot” learning?

Your Answer
Correct Answer

A) 1 example
B) 2-5 examples
C) 10-20 examples
D) 100+ examples

Question 10: Prompt Clarity

Which prompt demonstrates better clarity and specificity?

Your Answer
Correct Answer

A) “Write about dogs”
B) “Write a 300-word article about dog training for first-time owners”
C) “Tell me everything about dogs”
D) “Dogs are interesting, write something”

Question 11: Common Pitfalls

Which is an example of contradictory instructions?

Your Answer
Correct Answer

A) “Be brief but comprehensive”
B) “Write in a formal tone”
C) “Provide three examples”
D) “Explain step-by-step”

Question 12: Template Structures

What is the advantage of using the “Name:Content” format?

Your Answer
Correct Answer

A) It looks more professional
B) It provides clear structure and is easy to modify
C) It’s required by most LLMs
D) It reduces token usage

Question 13: Example Quality

In few-shot learning, what matters more?

Your Answer
Correct Answer

A) Quantity of examples
B) Quality and diversity of examples
C) Length of examples
D) Complexity of examples

Question 14: Output Format

Why should you specify output format in prompts?

Your Answer
Correct Answer

A) To make parsing and using the output easier
B) To make the prompt longer
C) To test the model’s capabilities
D) It’s not necessary

Question 15: Prompting vs Training

How does prompting differ from traditional model training?

Your Answer
Correct Answer

A) Prompting is slower than training
B) Prompting requires no parameter updates and enables immediate adaptation
C) Prompting only works for simple tasks
D) Prompting requires more computational resources

Part 2: Hands-On Project

Project: Restaurant Recommendation Chatbot

Objective: Create a complete prompt system for a restaurant recommendation chatbot that demonstrates all concepts from Module 1. Requirements: Your prompt system must include:

Role Assignment - Define the chatbot’s persona and expertise
In-Context Learning - Provide 2-3 example conversations
Clear Structure - Use proper formatting and delimiters
All Four Principles - Demonstrate clarity, reasoning guidance, context, and format

Scenario: Users will ask for restaurant recommendations based on:

Cuisine type
Price range
Dietary restrictions
Location/neighborhood
Occasion (casual, date night, business, family)

Project Template

[Your prompt system here]

Example structure:

ROLE:
[Define the chatbot's role and capabilities]

GUIDELINES:
[How should it handle queries?]

EXAMPLES:
[2-3 example conversations]

CONSTRAINTS:
[What should it avoid or always include?]

FORMAT:
[How should responses be structured?]

Sample Solution

View Sample Solution

ROLE:
You are RestaurantGuru, a knowledgeable and friendly restaurant 
recommendation assistant with expertise in diverse cuisines and 
dining experiences. You provide personalized recommendations based 
on user preferences.

GUIDELINES:
1. Always ask clarifying questions if preferences are unclear
2. Consider all stated requirements (cuisine, price, dietary needs)
3. Provide 2-3 specific recommendations with brief descriptions
4. Include key details: price range, atmosphere, signature dishes
5. Be enthusiastic but honest about each restaurant's strengths

EXAMPLES:

Example 1:
User: "I need a good Italian restaurant for a date night"
RestaurantGuru: "Great choice for a romantic evening! To give you 
the best recommendations, could you tell me:
- Your preferred neighborhood or area?
- Budget per person ($ = under $30, $$ = $30-60, $$$ = $60+)?
- Any dietary restrictions?

This will help me suggest the perfect spot!"

User: "Downtown, $$, no restrictions"
RestaurantGuru: "Perfect! Here are my top picks:

1. **Bella Notte** ($$)
   - Intimate candlelit atmosphere
   - Signature: Homemade pasta, truffle risotto
   - Why: Romantic ambiance, excellent wine list

2. **Trattoria Luna** ($$)
   - Cozy, authentic Italian
   - Signature: Wood-fired pizzas, osso buco
   - Why: Warm service, great for conversation

3. **Il Giardino** ($$$)
   - Upscale with garden patio
   - Signature: Seafood pasta, tiramisu
   - Why: Special occasion worthy, beautiful setting

Would you like more details about any of these?"

Example 2:
User: "Quick lunch spot, vegetarian, under $15"
RestaurantGuru: "I can help! Which area are you in, and do you 
prefer sit-down or grab-and-go?"

User: "Near the university, grab-and-go is fine"
RestaurantGuru: "Perfect for a quick campus lunch:

1. **Green Bowl** ($)
   - Build-your-own salad and grain bowls
   - Fast, fresh, lots of vegan options
   - 5-minute walk from main campus

2. **Falafel Express** ($)
   - Mediterranean wraps and plates
   - Generous portions, under $12
   - Popular with students

3. **Veggie Delight** ($)
   - Indian vegetarian cuisine
   - Daily lunch specials around $10
   - Flavorful, filling options

All three are quick and budget-friendly!"

CONSTRAINTS:
- Never recommend restaurants without considering stated preferences
- Always provide at least 2 options when possible
- Include price indicators ($, $$, $$$)
- Ask follow-up questions if critical information is missing
- Be honest if you need more details to give good recommendations

FORMAT:
Responses should include:
1. Acknowledgment of user's request
2. Clarifying questions (if needed)
3. Recommendations with:
   - Restaurant name and price range
   - Brief atmosphere description
   - Signature dishes
   - Why it fits their needs
4. Offer for additional information

Now, ready to help users find their perfect dining experience!

Project Evaluation Criteria

Your project will be evaluated on:

Role Definition

Clear persona with defined expertise and approach

Example Quality

Diverse, realistic examples showing different scenarios

Structure & Format

Well-organized with clear sections and delimiters

Principle Application

Demonstrates all four core principles effectively

Assessment Scoring

Multiple Choice (15 points)

1 point per correct answer
Passing: 12/15 (80%)

Hands-On Project (10 points)

Role Assignment: 2 points
In-Context Learning: 3 points
Structure & Format: 2 points
Principle Application: 3 points

Total Score

Maximum: 25 points
Passing: 20 points (80%)

Next Steps

Complete the Assessment

Answer all questions and complete the hands-on project

Review Your Answers

Check the correct answers and understand any mistakes

Achieve 80% or Higher

Ensure you understand the core concepts before proceeding

Move to Module 2

Ready to learn task-specific prompting patterns!

Congratulations! 🎉

You’ve completed Module 1: Foundations of Prompting! You now have a solid understanding of:

✅ The prompt-response paradigm and how LLMs work
✅ How to structure effective prompts with proper components
✅ Zero-shot, one-shot, and few-shot learning techniques
✅ The four core principles that make prompts effective

Review Module 1

Revisit any lessons you’d like to review

Start Module 2

Learn task-specific prompting patterns

Pro Tip: Keep the four core principles handy as a reference. You’ll apply them throughout Modules 2 and 3!

Module 1

Module 2

Module 3

​Assessment Overview

​Part 1: Multiple Choice Questions (15 questions)

​Question 1: Prompt-Response Paradigm

​Question 2: In-Context Learning

​Question 3: Prompt Components

​Question 4: Zero-Shot vs Few-Shot

​Question 5: Reasoning Guidance

​Question 6: Role Assignment

​Question 7: Context Provision

​Question 8: Format and Delimiters

​Question 9: Few-Shot Learning

​Question 10: Prompt Clarity

​Question 11: Common Pitfalls

​Question 12: Template Structures

​Question 13: Example Quality

​Question 14: Output Format

​Question 15: Prompting vs Training

​Part 2: Hands-On Project

​Project: Restaurant Recommendation Chatbot

​Project Template

​Sample Solution

​Project Evaluation Criteria

Role Definition

Example Quality

Structure & Format

Principle Application

​Assessment Scoring

​Multiple Choice (15 points)

​Hands-On Project (10 points)

​Total Score

​Next Steps

​Congratulations! 🎉

Review Module 1

Start Module 2

Assessment Overview

Part 1: Multiple Choice Questions (15 questions)

Question 1: Prompt-Response Paradigm

Question 2: In-Context Learning

Question 3: Prompt Components

Question 4: Zero-Shot vs Few-Shot

Question 5: Reasoning Guidance

Question 6: Role Assignment

Question 7: Context Provision

Question 8: Format and Delimiters

Question 9: Few-Shot Learning

Question 10: Prompt Clarity

Question 11: Common Pitfalls

Question 12: Template Structures

Question 13: Example Quality

Question 14: Output Format

Question 15: Prompting vs Training

Part 2: Hands-On Project

Project: Restaurant Recommendation Chatbot

Project Template

Sample Solution

Project Evaluation Criteria

Assessment Scoring

Multiple Choice (15 points)

Hands-On Project (10 points)

Total Score

Next Steps

Congratulations! 🎉