Complete Prompt Engineering Guide
Master the art and science of writing effective prompts to make AI better understand and respond to your needs
📚 Table of Contents
What is Prompt Engineering?
Prompt Engineering is the process of designing and optimizing input prompts, aiming to guide Large Language Models to generate desired outputs. Good prompts can significantly improve AI response quality and accuracy.
💡 Core Principle: Clear, specific, and structured prompts can achieve better results.
Clear Objective
Clearly state your desired results
Provide Context
Give AI sufficient background information
Iterative Optimization
Continuously improve prompts based on results
Core Techniques
1. Zero-shot Learning
Directly state task requirements without providing examples.
Translate the following text to English:
"Artificial Intelligence is changing the way we live."Output: Artificial intelligence is changing our way of life.
2. Few-shot Learning
Provide a few examples to guide the model to understand the task.
Sentiment Analysis Task:
Text: This movie is amazing!
Sentiment: Positive
Text: The service attitude is very poor.
Sentiment: Negative
Text: Product quality is okay, price is reasonable.
Sentiment: Neutral
Text: This shopping experience was very pleasant, will come again next time!
Sentiment: Output: Positive
3. Chain of Thought (CoT)
Guide the model to show its reasoning process, improving accuracy on complex tasks.
Question: If a store has 23 apples, sold 17, then restocked 45, how many apples are there now?
Let's think step by step:
1. Started with 23 apples
2. Sold 17: 23 - 17 = 6 apples
3. Restocked 45: 6 + 45 = 51 apples
Answer: The store now has 51 apples.4. Role Playing
Assign specific roles to AI to get specialized responses.
You are an experienced Python development expert, specializing in performance optimization and code refactoring.
Please help me optimize the performance of the following code:
def find_duplicates(lst):
duplicates = []
for i in range(len(lst)):
for j in range(i + 1, len(lst)):
if lst[i] == lst[j] and lst[i] not in duplicates:
duplicates.append(lst[i])
return duplicatesCommon Prompt Patterns
📋 Task Decomposition Pattern
1. [Step 1]
2. [Step 2]
3. [Step 3]
Suitable for complex multi-step tasks
🎯 Output Format Pattern
{
"field": "value"
}
Control output structure and format
🔍 Conditional Pattern
If [Condition B], then [Action B]
Otherwise, [Default Action]
Handle branching logic for different situations
📝 Template Filling Pattern
Topic: [X]
Key Points: [Y]
Conclusion: [Z]
Standardize output content structure
Advanced Techniques
🌡️ Temperature Parameter Adjustment
Low Temperature (0-0.3)
More deterministic, conservative output
Use for: Fact queries, code generation
Medium Temperature (0.5-0.7)
Balance creativity and accuracy
Use for: General conversation, translation
High Temperature (0.8-1.0)
More creative, diverse
Use for: Creative writing, brainstorming
🔗 Prompt Combination Techniques
# Combine multiple instructions
You are a professional content creator.
Task: Write an article about Artificial Intelligence
Requirements:
- Word count: About 500 words
- Style: Popular science, easy to understand
- Structure: Include introduction, main body (3 key points), conclusion
- Target audience: General readers interested in technology
Additional requirements:
- Use vivid examples
- Avoid too much technical jargon
- Maintain an objective and neutral tone🚫 Negative Prompting Technique
Explicitly tell AI what not to do to avoid unwanted output.
Example:
Write a product introduction, do not use exaggerated marketing language, do not make unrealistic promises, do not disparage competitors.
OptimizeStrategy
✨ CLEARFramework
Concise
Remove unnecessary words, keep it clear and concise
Logical
Ensure instructions have clear logical order
Explicit
Clearly state expected output format and content
Adaptive
Adjust and optimize prompts based on results
Reflective
Analyze output quality, continuously improve
Practical Examples
Example 1: Code Optimization
❌ Vague Prompt
✅ Optimized
1. Reduce time complexity
2. Optimize memory usage
3. Maintain code readability
4. Add necessary comments
[Code]
Example 2: Content Creation
❌ Vague Prompt
✅ Optimized
Topic: AI applications in healthcare
Word count: 800-1000 words
Include: 3 specific case studies
Tone: Professional but accessible
Structure: Introduction-Application cases-Challenges-Outlook
Example 3: Data Analysis
❌ Vague Prompt
✅ Optimized
1. Identify sales trends (monthly, quarterly)
2. Find best-performing product categories
3. Calculate year-over-year growth rate
4. Provide 3 actionable improvement suggestions
Output format: Summary + detailed analysis + chart description
Common Mistakes and Solutions
Too Broad
Prompts are too general, leading to unexpected output
Solution: Add specific requirements and constraints
Lack of Context
Not providing enough background information
Solution: Provide necessary background and relevant information
Contradictory Instructions
Prompts include conflicting requirements
Solution: Check and eliminate contradictions, maintain logical consistency
🎯 Core Points Summary
- Clear and specific task requirements
- Provide sufficient context information
- Use structured prompt format
- Choose appropriate techniques based on tasks
- Continuously test and optimize prompts
- Learn and accumulate best practices