🎯 Example Engineering: Teaching AI with the Right Examples
The Story of the Perfect Teacher
Imagine you’re teaching a puppy to fetch different toys. If you only show it tennis balls, it won’t know what to do with a frisbee! Example Engineering is the art of choosing and arranging the perfect “training toys” for AI.
🎪 The Big Picture
Think of prompt engineering like being a cooking teacher. You don’t just hand someone random recipes—you carefully pick which recipes, in what order, and how different they should be.
graph TD A[🎯 Example Engineering] --> B[📋 Example Ordering] A --> C[🌈 Diversity in Examples] A --> D[🎯 Active Selection] A --> E[📈 Difficulty Curriculum] A --> F[🔍 kNN Selection]
📋 Example Ordering: First Impressions Matter!
The Ice Cream Shop Story
You walk into an ice cream shop. The first flavor you taste changes everything! If you start with super sour lemon, even chocolate tastes weird after.
AI works the same way. The order of your examples shapes how AI understands the pattern.
✨ The Golden Rules
| Rule | Why It Works |
|---|---|
| Similar first | AI sees the pattern faster |
| Recent last | Last example sticks most |
| Build a story | Each example connects to the next |
🌟 Real Example
Bad Order:
Q: Cat? A: Animal
Q: 7+3? A: 10
Q: Dog? A: Animal
AI gets confused! Topics jump around.
Good Order:
Q: Cat? A: Animal
Q: Dog? A: Animal
Q: Fish? A: Animal
AI thinks: “Oh! I classify creatures!”
🎯 Quick Tip
Put your most important example last. It has the strongest influence!
🌈 Diversity in Examples: Don’t Be Boring!
The Pizza Party Problem
If you only ever eat pepperoni pizza, you’ll think ALL pizza is pepperoni. Then someone hands you a veggie pizza and you’re lost!
Diverse examples = smarter AI.
🎨 Types of Diversity
graph TD D[🌈 Diversity] --> A[Different Topics] D --> B[Different Lengths] D --> C[Different Styles] D --> E[Edge Cases]
✨ The Magic Mix
Monotonous (Bad):
- Happy review → Positive
- Great product → Positive
- Love it → Positive
Diverse (Good):
- “Amazing product!” → Positive
- “Terrible experience” → Negative
- “It’s okay I guess” → Neutral
- “Best thing ever!!!” → Positive
🌟 Why It Works
| Monotonous | Diverse |
|---|---|
| AI memorizes | AI learns |
| Fails on new cases | Handles anything |
| Narrow thinking | Flexible thinking |
Remember: Show AI the whole rainbow, not just one color!
🎯 Active Example Selection: Be a Smart Picker!
The Detective Story
Imagine you’re a detective picking witnesses. You don’t grab random people—you find the BEST ones who saw the most important things.
Active selection = picking examples that teach the most.
🔍 How It Works
graph TD A[🤔 AI is Confused] --> B[Find Confusing Area] B --> C[Pick Example That Helps] C --> D[AI Learns Better] D --> A
✨ The Three Questions
Before picking an example, ask:
- Is this NEW? Does it teach something different?
- Is this CLEAR? Will AI understand it easily?
- Is this HELPFUL? Does it fill a gap?
🌟 Real Example
AI keeps mixing up “questions” and “statements.”
Active Pick:
"Is this a cat?" → Question
"This is a cat." → Statement
"Can you help?" → Question
"I can help." → Statement
You picked examples that TARGET the confusion!
🎯 Quick Tip
Watch where AI makes mistakes. Pick examples that fix THOSE spots!
📈 Difficulty Curriculum: Start Easy, Level Up!
The Video Game Story
Every great video game starts with easy levels. You learn to jump before fighting the boss! If Level 1 was the final boss, you’d quit immediately.
Curriculum = organizing examples from easy to hard.
🎮 The Learning Path
graph TD A[🟢 Super Easy] --> B[🟡 A Little Harder] B --> C[🟠 Getting Tricky] C --> D[🔴 Challenge Time!]
✨ Building Blocks
| Level | Example Type | Why |
|---|---|---|
| 1 | Simple, clear | Build confidence |
| 2 | Slightly complex | Stretch thinking |
| 3 | Tricky cases | Handle edge cases |
| 4 | Real-world messy | Master the skill |
🌟 Real Example: Sentiment Analysis
Level 1 - Crystal Clear:
"I love this!" → Positive
"I hate this!" → Negative
Level 2 - A Bit Harder:
"Pretty good overall" → Positive
"Could be better" → Negative
Level 3 - Tricky:
"Not bad at all" → Positive
"I don't hate it" → Neutral
Level 4 - Expert:
"Sarcastically great" → Negative
"Guilty pleasure" → Mixed
🎯 Quick Tip
Never throw hard examples first. Let AI build confidence!
🔍 kNN Example Selection: Finding Neighbors!
The Playground Story
When you need help with homework, you don’t ask someone from another school. You ask your closest friends who are working on similar stuff!
kNN = “k Nearest Neighbors”
It means: find examples most SIMILAR to your new problem.
🏘️ How kNN Works
graph TD A[❓ New Question] --> B[🔍 Search Examples] B --> C[📏 Measure Similarity] C --> D[✅ Pick Closest Ones] D --> E[🎯 Better Answer!]
✨ The Similarity Game
Your question: “What’s the capital of France?”
| Example | Similarity |
|---|---|
| “Capital of Germany? Berlin” | ⭐⭐⭐ Very close! |
| “Best pizza topping? Cheese” | ⭐ Not related |
| “Capital of Spain? Madrid” | ⭐⭐⭐ Very close! |
kNN picks the geography examples!
🌟 Why It’s Powerful
| Random Examples | kNN Examples |
|---|---|
| Hit or miss | Always relevant |
| Wastes space | Efficient |
| Confuses AI | Focuses AI |
🎯 The k in kNN
k = how many neighbors to pick
- k=1: Just the closest one
- k=3: Top 3 closest
- k=5: Top 5 closest
Tip: k=3 to 5 usually works great!
🌟 Real Example
User asks: “How do I make pasta?”
kNN finds these similar examples:
Q: How do I make spaghetti?
A: Boil water, add pasta...
Q: How do I cook noodles?
A: Heat water, add noodles...
Q: Best pasta cooking time?
A: Usually 8-12 minutes...
AI now has RELEVANT context!
🎓 Putting It All Together
The Master Recipe
graph TD A[🎯 Your Task] --> B[🔍 kNN: Find Similar] B --> C[🌈 Add Diversity] C --> D[📈 Order by Difficulty] D --> E[📋 Arrange Thoughtfully] E --> F[✨ Perfect Examples!]
🏆 The Five Powers Combined
| Technique | Superpower |
|---|---|
| Ordering | Controls attention |
| Diversity | Prevents blind spots |
| Active Selection | Fixes weaknesses |
| Curriculum | Builds confidence |
| kNN | Finds relevance |
🌟 Your Example Engineering Checklist
Before sending your prompt, ask:
- [ ] Are my examples in a logical ORDER?
- [ ] Do I have DIVERSE examples?
- [ ] Did I ACTIVELY pick helpful ones?
- [ ] Do they go from EASY to HARD?
- [ ] Are they SIMILAR to my actual question?
💡 Remember This!
Example Engineering is like being a great teacher.
You don’t just throw information at students. You carefully choose what to show, in what order, with the right variety, building from simple to complex, always staying relevant.
That’s the secret to making AI truly understand! 🚀
You now know the five core techniques of Example Engineering. Time to practice!