Time Estimate: 45-60 minutes
Passing Score: 80% (16/20 questions)
Passing Score: 80% (16/20 questions)
Assessment Overview
This assessment evaluates your understanding of task-specific prompting patterns covered in Module 2:- Text classification techniques
- Information extraction methods
- Content generation strategies
- Text transformation approaches
- Question-answering systems
This assessment includes both multiple-choice questions and a hands-on capstone project. Take your time and demonstrate what you’ve learned!
Part 1: Multiple Choice Questions (20 questions)
Question 1: Classification Fundamentals
What is the primary challenge when using LLMs for classification tasks?- Your Answer
- Correct Answer
A) LLMs are too slow for classification
B) LLMs generate text probabilistically, leading to inconsistent output formats
C) LLMs cannot understand categories
D) LLMs require fine-tuning for every classification task
B) LLMs generate text probabilistically, leading to inconsistent output formats
C) LLMs cannot understand categories
D) LLMs require fine-tuning for every classification task
Question 2: Few-Shot Classification
When using few-shot classification, what is the recommended number of examples per category?- Your Answer
- Correct Answer
A) 1 example
B) 2-5 examples
C) 10-20 examples
D) 50+ examples
B) 2-5 examples
C) 10-20 examples
D) 50+ examples
Question 3: Named Entity Recognition
What is the advantage of progressive extraction (simple → complex) in NER tasks?- Your Answer
- Correct Answer
A) It’s faster than single-step extraction
B) It improves accuracy by building complexity gradually
C) It requires less context
D) It works better with small models
B) It improves accuracy by building complexity gradually
C) It requires less context
D) It works better with small models
Question 4: Structured Output
Why should you provide a JSON schema when requesting JSON output?- Your Answer
- Correct Answer
A) It makes the model run faster
B) It ensures consistent structure and field names
C) It’s required by the API
D) It reduces token usage
B) It ensures consistent structure and field names
C) It’s required by the API
D) It reduces token usage
Question 5: Content Generation
What is the most effective way to control the tone of generated content?- Your Answer
- Correct Answer
A) Use a higher temperature setting
B) Explicitly specify the desired tone in the prompt
C) Provide longer context
D) Use more examples
B) Explicitly specify the desired tone in the prompt
C) Provide longer context
D) Use more examples
Question 6: Constrained Generation
Which constraint is most effective for controlling output length?- Your Answer
- Correct Answer
A) “Keep it short”
B) “Be concise”
C) “Write exactly 150 words”
D) “Don’t write too much”
B) “Be concise”
C) “Write exactly 150 words”
D) “Don’t write too much”
Question 7: Code Generation
What should you always include when generating code with LLMs?- Your Answer
- Correct Answer
A) Comments explaining the code
B) Error handling and input validation
C) Type hints or type annotations
D) All of the above
B) Error handling and input validation
C) Type hints or type annotations
D) All of the above
Question 8: Multi-Step Generation
Why is multi-step generation often more effective than single-step for complex content?- Your Answer
- Correct Answer
A) It’s faster
B) It allows for better structure and organization
C) It uses fewer tokens
D) It requires less context
B) It allows for better structure and organization
C) It uses fewer tokens
D) It requires less context
Question 9: Translation Patterns
When translating marketing content, what additional information should you provide?- Your Answer
- Correct Answer
A) Only the source and target languages
B) Context, tone, and cultural considerations
C) A dictionary of terms
D) Multiple translation options
B) Context, tone, and cultural considerations
C) A dictionary of terms
D) Multiple translation options
Question 10: Summarization Strategy
What’s the difference between extractive and abstractive summarization?- Your Answer
- Correct Answer
A) Extractive is shorter than abstractive
B) Extractive pulls key sentences; abstractive rephrases and synthesizes
C) Extractive is more accurate than abstractive
D) Abstractive requires more examples
B) Extractive pulls key sentences; abstractive rephrases and synthesizes
C) Extractive is more accurate than abstractive
D) Abstractive requires more examples
Question 11: Style Transfer
When adapting text for different reading levels, what should you adjust?- Your Answer
- Correct Answer
A) Only vocabulary
B) Only sentence length
C) Vocabulary, sentence complexity, and concept abstraction
D) Only the tone
B) Only sentence length
C) Vocabulary, sentence complexity, and concept abstraction
D) Only the tone
Question 12: Format Transformation
What’s the best approach for converting a paragraph into FAQ format?- Your Answer
- Correct Answer
A) Ask the model to “make it a FAQ”
B) Specify the number of Q&A pairs and their focus
C) Provide one example FAQ
D) Just extract questions from the text
B) Specify the number of Q&A pairs and their focus
C) Provide one example FAQ
D) Just extract questions from the text
Question 13: Question-Answering Context
Why is providing context crucial for QA tasks?- Your Answer
- Correct Answer
A) It makes responses longer
B) It grounds answers in specific information and reduces hallucinations
C) It’s required by the model
D) It improves response speed
B) It grounds answers in specific information and reduces hallucinations
C) It’s required by the model
D) It improves response speed
Question 14: Math Problem Solving
What is the purpose of the GSM8K annotation format (using «calculation»)?- Your Answer
- Correct Answer
A) It makes the output look professional
B) It helps track intermediate steps and catch errors
C) It’s required for mathematical operations
D) It reduces token usage
B) It helps track intermediate steps and catch errors
C) It’s required for mathematical operations
D) It reduces token usage
Question 15: Complex Question Decomposition
When should you break a question into sub-questions?- Your Answer
- Correct Answer
A) Always, for every question
B) Only for math problems
C) When the question has multiple components or requires multi-step reasoning
D) Never, it’s inefficient
B) Only for math problems
C) When the question has multiple components or requires multi-step reasoning
D) Never, it’s inefficient
Question 16: Handling Uncertainty
What should a QA system do when it doesn’t have enough information to answer?- Your Answer
- Correct Answer
A) Make an educated guess
B) Provide a partial answer
C) Explicitly state that the information is not available
D) Search for the answer online
B) Provide a partial answer
C) Explicitly state that the information is not available
D) Search for the answer online
Question 17: Multi-Label Classification
How does multi-label classification differ from multi-class classification?- Your Answer
- Correct Answer
A) Multi-label is more accurate
B) Multi-label allows items to belong to multiple categories simultaneously
C) Multi-label requires more examples
D) Multi-label is faster
B) Multi-label allows items to belong to multiple categories simultaneously
C) Multi-label requires more examples
D) Multi-label is faster
Question 18: Extraction Validation
Why should you request confidence levels for critical extractions?- Your Answer
- Correct Answer
A) It makes the output longer
B) It helps identify uncertain extractions that may need human review
C) It’s required for structured output
D) It improves extraction accuracy
B) It helps identify uncertain extractions that may need human review
C) It’s required for structured output
D) It improves extraction accuracy
Question 19: Generation Attributes
Which attribute specification is most effective for marketing copy?- Your Answer
- Correct Answer
A) “Write good marketing copy”
B) “Target: fitness enthusiasts, Tone: energetic, Length: 150 words, Include: CTA”
C) “Make it sound professional”
D) “Write something catchy”
B) “Target: fitness enthusiasts, Tone: energetic, Length: 150 words, Include: CTA”
C) “Make it sound professional”
D) “Write something catchy”
Question 20: Transformation Fidelity
What’s the key balance in text transformation?- Your Answer
- Correct Answer
A) Speed vs. accuracy
B) Length vs. detail
C) Fidelity to source vs. adaptation to target
D) Creativity vs. consistency
B) Length vs. detail
C) Fidelity to source vs. adaptation to target
D) Creativity vs. consistency
Part 2: Capstone Project - Content Moderation System
Important: This hands-on project tests your ability to combine multiple prompting patterns from Module 2.
Project Overview
Build a comprehensive content moderation system that:- Classifies content safety levels
- Extracts problematic elements
- Generates explanations
- Suggests modifications for reviewed content
Project Requirements
Your system should handle social media posts and classify them into three categories:- Safe: Appropriate for all audiences, no policy violations
- Review: Potentially problematic, needs human review
- Unsafe: Clear policy violations (hate speech, violence, explicit content, harassment)
Task 1: Classification Prompt (25 points)
Design a prompt that classifies content into Safe/Review/Unsafe categories. Requirements:- Include clear definitions for each category
- Provide 2-3 examples per category
- Request both classification and reasoning
- Handle edge cases
Sample Solution
Sample Solution
Task 2: Extraction Prompt (25 points)
For posts classified as “Review” or “Unsafe,” extract specific problematic elements. Requirements:- Identify problematic words/phrases
- Categorize the type of violation
- Extract context that might affect classification
- Use structured output (JSON format)
Sample Solution
Sample Solution
Task 3: Explanation Generation (25 points)
Generate clear explanations for why content was flagged. Requirements:- Explain the classification decision
- Reference specific policy violations
- Use appropriate tone (firm but not accusatory)
- Provide educational value
Sample Solution
Sample Solution
Task 4: Modification Suggestions (25 points)
For “Review” content, suggest how it could be modified to be acceptable. Requirements:- Preserve the user’s core message when possible
- Provide 2-3 specific modification options
- Explain what makes each modification acceptable
- Maintain the user’s voice
Sample Solution
Sample Solution
Complete System Integration
Combine all four components into a single, comprehensive moderation workflow:Complete System Prompt
Complete System Prompt
Testing Your System
Test your complete system with these sample posts:- Safe Example: “Just adopted a rescue dog! Meet Charlie 🐕”
- Review Example: “Can’t believe how stupid some people are. This country is going downhill fast.”
- Unsafe Example: “I know where you live and I’m coming for you”
Expected Outputs
Expected Outputs
Post 1 - Safe:
- Classification: Safe
- Reasoning: Positive personal update, no policy concerns
- No extraction needed
- Brief confirmation of safety
- Classification: Review
- Reasoning: Strong negative opinion with potentially offensive language, but no direct threats
- Extraction: “stupid” (low severity), generalized criticism
- Suggestions: Rephrase without insulting language
- Classification: Unsafe
- Reasoning: Direct threat with specific intent to harm
- Extraction: Threat of violence, specific target
- Explanation: Clear policy violation, immediate action required
- No modification suggestions (content cannot be salvaged)
Scoring Rubric
Multiple Choice (60 points)
- 3 points per question
- 16/20 correct required to pass (48/60 points)
Capstone Project (40 points)
- Task 1 (Classification): 10 points
- Task 2 (Extraction): 10 points
- Task 3 (Explanation): 10 points
- Task 4 (Modification): 10 points
Passing Score: 80 points
Evaluation Criteria
Your capstone project will be evaluated on:Accuracy
Correct classification and extraction of problematic elements
Clarity
Clear, understandable explanations and suggestions
Completeness
All required components included and properly structured
Practicality
System is usable and produces actionable results
Next Steps
Complete all 20 multiple-choice questions
Build and test your content moderation system
Verify your system handles all three classification categories
Test with edge cases and ambiguous content
Continue to Module 3: Advanced Techniques
Master Chain of Thought, decomposition, and RAG