🎯 What is Prompt Engineering?
Prompt Engineering is the practice of designing and refining inputs to get optimal outputs from LLMs. It's the difference between mediocre AI responses and production-ready results.
The 80/20 Rule: 80% of LLM output quality comes from 20% effort in prompt design. Master these techniques to 10x your AI application quality.
🎨 Prompt Patterns
Zero-Shot
Direct instruction without examples
Use when: Simple, well-defined tasks
❌ Poor Prompt
Write about dogs
✅ Good Prompt
Write a 200-word informative paragraph about Golden Retrievers, focusing on their temperament and suitability as family pets
Template: [Task] + [Constraints] + [Format]
📚 Prompt Frameworks
CLEAR Framework
Context, Length, Examples, Audience, Response
Context
Provide background information
Length
Specify desired output length
Examples
Show what you want
Audience
Define who will read it
Response
Specify format and style
Example:
Context: I'm building a React app for task management Length: 100-150 words Examples: Like Todoist or Notion tasks Audience: Junior developers Response: Bullet points with code snippets
🚀 Advanced Techniques
🎯 Prompt Chaining
Break complex tasks into multiple prompts
Prompt 2: "Organize by theme"
Prompt 3: "Write summary"
🔄 Self-Consistency
Generate multiple outputs and vote
Compare outputs
Use majority answer
🎭 Perspective Prompting
Get diverse viewpoints
"As a user..."
"As a security expert..."
🏗️ Scaffolding
Provide structure for complex outputs
"Key points: •"
"### Section 2: [Topic]"
🔧 Prompt Improver
❌ Common Mistakes
Being too vague
❌ Bad
Tell me about AI
✅ Good
Explain how transformer neural networks enable large language models, focusing on the attention mechanism
Impact: Unfocused, rambling responses
No success criteria
❌ Bad
Write good code
✅ Good
Write Python code that is PEP 8 compliant, includes type hints, has docstrings, and handles errors gracefully
Impact: Subjective, inconsistent quality
Overloading the prompt
❌ Bad
Write a blog post about AI and ML and deep learning and also explain transformers and GPT and BERT and...
✅ Good
Write a 500-word blog post explaining transformers to software engineers new to ML
Impact: Confused, incomplete responses
Assuming knowledge
❌ Bad
Fix the bug in the auth flow
✅ Good
Here's my authentication code: [code]. Users report login fails after password reset. Debug and fix:
Impact: Generic advice instead of specific solutions
✅ Best Practices
Do's
- ✓ Be specific and detailed
- ✓ Provide examples when possible
- ✓ Specify output format
- ✓ Set clear constraints
- ✓ Test and iterate prompts
- ✓ Version control prompts
Don'ts
- ✗ Use ambiguous language
- ✗ Assume context
- ✗ Overload single prompts
- ✗ Ignore token limits
- ✗ Skip validation
- ✗ Use one prompt for all cases
🎯 Key Takeaways
Specificity wins: Clear, detailed prompts get better results than clever ones
Use frameworks: CLEAR, RISE, or STAR provide consistent structure
Examples > Instructions: Show don't tell with few-shot prompting
Iterate and test: A/B test prompts like you would UI changes
Version control: Track prompt changes and performance