Introduction: The Next Level of Prompt Engineering

Welcome to the fourth article in the prompt engineering series. Having covered basic prompt writing techniques and the characteristics of ChatGPT and Claude, it's now time to dive into advanced techniques. Honestly, the techniques we'll discuss today might seem somewhat complex at first. However, once you master them, your AI utilization skills will noticeably improve.

When I first learned these techniques, I had many questions like "Why does this method work?" Understanding "why this approach produces better results" rather than just "do it this way" is crucial. That's how you can adapt them to different situations. So in this article, I'll explain the principles behind each technique along with detailed practical examples.

1. Chain of Thought (CoT) Prompting

1.1 What is CoT?

Chain of Thought, or CoT, is a technique that asks AI to show its "thinking process" step by step. This method, first systematized in a 2022 Google research paper, showed remarkable performance improvements especially in math problems and tasks requiring logical reasoning.

The principle is simple. When people solve complex problems, they don't arrive at the answer all at once - they go through intermediate steps, right? AI works the same way. When you make it explicitly generate intermediate steps, the accuracy of the final answer increases significantly.

1.2 Basic CoT Application

The simplest method is to add "Think step by step" or "Let's think step by step" at the end of your prompt.

Without CoT:

John had 5 apples. He gave 3 to Mary,
bought 7 more at the market, and then gave away half of them to friends.
How many apples does John have left?

With CoT:

John had 5 apples. He gave 3 to Mary,
bought 7 more at the market, and then gave away half of them to friends.
How many apples does John have left?

Please show your calculation process step by step.

With this approach, the AI responds like this:

Let me calculate step by step:

Step 1: John's initial apples = 5
Step 2: After giving 3 to Mary = 5 - 3 = 2 apples
Step 3: After buying 7 more at market = 2 + 7 = 9 apples
Step 4: After giving half to friends = 9 / 2 = 4.5 apples

If we interpret as whole numbers, either 4 or 5 remain.
(If you can't split one, give away 4 and keep 5)

1.3 Zero-shot CoT vs Few-shot CoT

Zero-shot CoT only gives the instruction "think step by step" without examples. It's simple and quick to apply, but may have limitations with complex problems.

Few-shot CoT shows examples of solving similar problems first. Since AI can learn and apply the thinking pattern, it produces more accurate results.

Please solve problems in the following manner:

[Example Problem]
Tom had $100. He spent $35 on a book,
and lent $50 to a friend for lunch. Later the friend paid back the $50.
How much money does Tom have left?

[Solution]
Step 1: Initial amount = $100
Step 2: After book purchase = $100 - $35 = $65
Step 3: After lending to friend = $65 - $50 = $15
Step 4: After friend repays = $15 + $50 = $65
Answer: $65

[Actual Problem]
(Enter the actual problem you want to solve here)

1.4 When CoT is Effective

  • Math calculations: Problems requiring multiple calculation steps
  • Logic puzzles: Problems where conditions must be applied sequentially
  • Code debugging: Cases requiring program execution flow tracking
  • Causal analysis: Cases requiring identification of cause-and-effect chains
  • Complex decision-making: Choices requiring consideration of multiple factors

2. Few-shot Learning Technique

2.1 The Core of Few-shot Learning

Few-shot Learning is a technique that shows AI a few examples and has it respond to new inputs following that pattern. Think of it as showing someone "do it like this" with examples.

This technique is powerful because it allows you to convey styles or formats through examples that are difficult to explain explicitly. For instance, rather than saying "write in a natural and friendly tone," showing 3 examples in that tone yields more accurate results.

2.2 Effective Example Construction

Number of examples: Generally 3-5 is appropriate. Too few makes pattern recognition difficult, too many wastes context.

Example diversity: Including various types produces more robust results.

Example quality: Provide examples representing the ideal form of your desired output.

Here are examples of analyzing product reviews:

[Example 1]
Review: "Shipping was too slow. It took a whole week. The product itself is okay though..."
Analysis:
- Sentiment: Negative
- Main complaint: Shipping speed
- Product satisfaction: Average
- Needs improvement: Shipping system

[Example 2]
Review: "Excellent value for money! Amazing quality at this price."
Analysis:
- Sentiment: Very positive
- Main satisfaction: Value for money
- Product satisfaction: High
- Strength: Price competitiveness

[Example 3]
Review: "It's just okay. Nothing particularly good or bad."
Analysis:
- Sentiment: Neutral
- Notable: None
- Product satisfaction: Average
- Improvement suggestion: Differentiation point needed

Now analyze the following review in the same format:
"It was good at first but broke after a month. The warranty process was too complicated."

2.3 Zero-shot vs Few-shot Comparison

Aspect Zero-shot Few-shot
Definition Perform with instructions only, no examples Perform after learning patterns from examples
Advantages Quick and simple Higher accuracy, easier format control
Disadvantages Difficult to convey complex formats Requires example preparation, token consumption
Best for Simple, clear tasks Tasks where specific format/style matters

3. Self-Consistency Technique

3.1 Concept and Principle

Self-Consistency performs multiple reasoning attempts on the same question and selects the most frequent answer. Think of it as democratic voting.

This technique is effective because AI responses have some degree of randomness. The same question can lead through slightly different reasoning paths, but if multiple paths reach the same conclusion, that answer is likely correct.

3.2 Practical Application

For the following problem, solve it 3 times using different approaches,
and compare the results from each solution.

Problem: A company's sales this quarter increased 15% compared to last quarter,
and last quarter's sales decreased 20% compared to the quarter before that.
Calculate what percentage of the quarter-before-last's sales this quarter's sales represent.

After each approach:
1. Calculation result
2. Confidence level (High/Medium/Low)
3. Points other approaches might miss

Please specify these. Then finally select the most reliable answer.

3.3 Self-Consistency Tips

  • Automating through API is efficient (increase temperature slightly for diversity)
  • Especially effective for complex math or logic problems
  • Takes more time and cost, but useful when accuracy is critical

4. Tree of Thoughts (ToT) Technique

4.1 Evolution from CoT

Tree of Thoughts is an extended concept of Chain of Thought. While CoT follows a single thinking path, ToT explores multiple possible thinking paths branching out like a tree.

Think about chess. When making a move, you think "if I move here, the opponent responds this way, then I can..." considering multiple possibilities. ToT works the same way. It explores multiple possibilities, evaluates each path, and selects the best direction.

4.2 ToT Prompt Construction

Please approach the following problem using Tree of Thoughts method.

Problem: Our startup's monthly marketing budget is $50,000.
We need to acquire 1,000 new users within 3 months.
What channel combination would be most effective?

Step 1: List 3 major marketing channels and explain their characteristics.

Step 2: For each channel combination (A only, B only, C only, A+B, A+C, B+C, A+B+C):
- Estimated cost
- Estimated reach
- Estimated conversion rate
Please estimate.

Step 3: Evaluate pros and cons of each combination. (Score 1-10)
- Cost efficiency
- Goal achievement probability
- Risk level

Step 4: Select the 2 most promising combinations for in-depth analysis.

Step 5: Present final recommendation and execution plan.

4.3 When ToT Shines

  • Strategy development: When systematically comparing multiple alternatives
  • Creative problem-solving: Open problems without a single right answer
  • Games/Puzzles: Situations requiring multiple moves ahead
  • Complex decision-making: Choices with many trade-offs

5. ReAct (Reasoning + Acting) Pattern

5.1 Core Concept of ReAct

ReAct is a pattern that alternates between "Reasoning" and "Acting." The AI doesn't just think - it gathers information and refines its reasoning through intermediate "actions" (searching, calculating, verifying, etc.).

We actually solve complex problems this way too. "Let me first verify if this is true" -> Verify -> "Ah, I see. Then I should go this direction" -> Next action. This cycle repeats.

5.2 ReAct Prompt Format

Please answer the following question using the ReAct method.

Question: "How has AI startup investment trends in the US changed over the past 3 years?"

Follow this format:

Thought 1: [Identify what you know and what information you need]
Action 1: [Action to obtain information - e.g., search, calculate, compare]
Observation 1: [Information obtained from the action]

Thought 2: [Reasoning based on new information]
Action 2: [Next required action]
Observation 2: [Result of action]

... (Repeat as needed)

Final Answer: [Comprehensive final answer]

5.3 Advantages of the ReAct Pattern

  • Transparency: You can clearly see on what basis conclusions were reached
  • Correctability: You can discover and correct errors in the intermediate process
  • External tool integration: Naturally combines with search, calculators, API calls, etc.
  • Improved reliability: Conclusions based on verified information, not guesses

6. Meta-Prompt Writing

6.1 What is a Meta-Prompt?

A meta-prompt is a "prompt that creates prompts." You ask AI to write a good prompt. It's very useful when you're unfamiliar with prompt writing or need prompts optimized for specific tasks.

6.2 Effective Meta-Prompts

I want to write an effective prompt for [specific task].

Background:
- AI to use: Claude 3.5 Sonnet
- Task purpose: Writing technical blog posts
- Target audience: Non-technical people interested in IT
- Desired tone: Friendly but professional
- Article length: 2000-3000 words

Requirements:
1. Suggest the most suitable prompt structure for this task
2. List the key elements to include
3. Tell me pitfalls to avoid or common mistakes
4. Create a practical prompt template
5. Also present 2 variations that could improve this prompt

6.3 Meta-Prompt Use Cases

  • Creating prompt templates when starting a new type of task
  • Requesting improvements when existing prompts aren't performing satisfactorily
  • Writing standardized prompt guidelines to share with the team
  • Customizing prompts for specific domains (legal, medical, finance, etc.)

7. Prompt Chaining Strategy

7.1 Concept of Chaining

Prompt chaining is a strategy that breaks complex tasks into multiple smaller prompts executed sequentially. The result of one prompt becomes the input for the next.

Why break it up this way? First, you can control each step more precisely. Second, you can check intermediate results and modify if needed. Third, you can improve the quality of complex tasks.

7.2 Chaining Design Example

Scenario: Creating a Competitor Analysis Report

Chain 1: Information Gathering
"For the following 3 companies (A, B, C), organize their main products,
recent news, and market positioning information."

(Result feeds into next prompt)

Chain 2: Comparative Analysis
"Based on the above information, compare the three companies on:
- Product strengths/weaknesses
- Pricing strategy
- Target customers"

Chain 3: Insight Derivation
"Based on the comparative analysis, suggest 3 differentiation strategies
our company could pursue."

Chain 4: Report Writing
"Synthesize all the above content into a 2-page executive report.
Include key insights and action items."

7.3 Tips for Effective Chaining

  • Specify clear output format: Request output in a format suitable as input for the next step
  • Intermediate verification points: Check results at each step and re-run if necessary
  • Maintain context: Design so necessary context passes to the next step
  • Error handling: Prepare alternatives when one step fails

8. Practical Advanced Prompt Examples

8.1 Code Review Prompt

You are a senior software engineer with 15 years of experience.
You have deep understanding of SOLID principles and clean code.

Please review the following code:

```python
[code input]
```

Review format:
1. Overall evaluation (Grade A/B/C/D)
2. Strengths (2-3 specific items)
3. Areas needing improvement (in priority order)
   - For each: Problem, why it's a problem, how to improve
4. Security vulnerability check (if any)
5. Performance optimization suggestions (if any)
6. Improved code version (full or key portions)

Please give honest but constructive feedback.
Explain so that junior developers can also understand.

8.2 Market Analysis Prompt

[Background]
I am a business development manager at a B2B SaaS startup.
We are considering entering a new market.

[Analysis Request]
Please analyze the "HR Tech market for SMBs in the US" using Tree of Thoughts approach.

[Analysis Framework]
1. Market size and growth estimation
   - Optimistic/Normal/Pessimistic scenarios

2. Major player analysis
   - Each player's strengths/weaknesses
   - Competitive intensity assessment

3. Customer segment analysis
   - Needs and pain points by segment
   - Selection of most promising target segment

4. Market entry strategy options
   - Derive at least 3 options
   - Risk/Return analysis for each option
   - Final recommendation

[Output Format]
Separate each section with clear headings.
Highlight key insights in bold.
Clearly indicate where data is uncertain.

8.3 Complex Writing Prompt

[Role]
You are a veteran tech journalist with 10 years of IT media experience.
You're known for your ability to explain complex technology in ways general audiences understand.

[Task]
Write an in-depth analysis article on "The Impact of Generative AI
on the Software Development Industry."

[Requirements]
- Length: 3000-4000 words
- Tone: Professional but accessible
- Target audience: Non-developers interested in development, startup founders

[Structure]
1. Attention-grabbing lead (start with real case or statistic)
2. Current situation (Impact of GitHub Copilot, ChatGPT, etc.)
3. Changes in developer jobs (balance positive views and concerns)
4. Industry expert opinions (interview format with 3+ fictional experts)
5. Future outlook (short-term 1-2 years, mid-term 5 years)
6. Implications for readers

[Additional Guidelines]
- Avoid excessive technical jargon (add simple explanation when used)
- Avoid one-sided perspectives
- Include specific figures or cases
- Use subheadings throughout for readability

8.4 Problem-Solving Prompt

[Situation]
Our company's mobile app retention rate is 15% at 30 days.
The industry average is 25%. We need to solve this problem.

[Available Data]
- DAU: 10,000 users
- Main churn point: 60% within first 3 days
- Main features used: A(45%), B(30%), C(25%)
- User feedback: "Too complicated", "Don't see the value", "Too many notifications"

[Request]
Please analyze this problem and propose solutions using the ReAct pattern.

At each step:
Thought: Current hypothesis or analysis
Action: What to verify or analyze
Observation: Analysis result

Specify these, and derive the final solution through at least 5 cycles.

The solution should include:
1. Root cause analysis
2. Short-term solutions (executable within 1 week)
3. Mid-term solutions (1 month plan)
4. Success metrics (KPIs)
5. Expected risks and mitigation plans

8.5 Learning Material Generation Prompt

[Purpose]
Create learning materials on "List Comprehension" for Python beginners.

[Target]
- Knows basic Python syntax (variables, conditionals, loops)
- Less than 3 months programming experience
- Learning goal: Use list comprehension proficiently in practice

[Few-shot Example Format]
Explain each concept with this structure:

Concept: [One-line definition]
Why needed: [Real-world necessity]
Basic syntax: [Code block]
Simple example:
  - Regular for-loop version
  - List comprehension version
  - Explanation of differences
Practice problems: [2 problems to solve]
Common mistakes: [Things to watch out for]

[Request]
Using the above format, explain in this order:
1. Basic list comprehension
2. List comprehension with conditionals
3. Nested list comprehension
4. Dictionary/set comprehension
5. 3 practical examples

Add a guideline at the end for "when to use and when not to use"

Conclusion: Keys to Mastering Advanced Techniques

We've explored various advanced prompt techniques: Chain of Thought, Few-shot Learning, Tree of Thoughts, ReAct pattern, meta-prompts, and prompt chaining. While they may seem complex at first, the core is ultimately one thing: systematically helping AI produce better results.

Here are some tips for effectively utilizing these techniques:

  • Choose techniques appropriate for the situation: You don't need to apply every technique to every situation. Using complex techniques for simple tasks is actually inefficient.
  • Combine techniques: Combining CoT + Few-shot, ToT + ReAct, etc. can yield more powerful results.
  • Iterative improvement: Don't try to write the perfect prompt from the start. It's more effective to gradually improve based on results.
  • Record and organize: Save prompts that worked well and analyze why they worked. Over time, you'll build your own prompt library.

Prompt engineering is ultimately "effective communication skills with AI." The techniques learned today are tools that make that communication more sophisticated and effective. Apply them one by one to your actual work and develop your own style.

In the next article, we'll cover practical applications of prompt engineering along with prompt strategies optimized for each industry. Feel free to leave any questions or experiences you'd like to share in the comments.