Advanced AI Prompting Techniques

Beyond basic prompts lie advanced techniques that dramatically improve AI outputs. These methods leverage model capabilities, structure reasoning, and guide AI toward better results.

Chain-of-Thought Prompting

Basic Chain-of-Thought

Encourage step-by-step reasoning:

Solve this problem step by step:

Problem: A store has 15 apples. They sell 3 apples each day. 
How many days until they have 6 apples left?

Step 1: Calculate how many apples need to be sold
Step 2: Calculate how many days that takes
Step 3: Provide the final answer

Zero-Shot Chain-of-Thought

Add "Let's think step by step" to prompts:

Question: What is 25 * 17?

Let's think step by step:

Few-Shot Chain-of-Thought

Provide examples with reasoning:

Example 1:
Question: Jane has 5 apples. She gives 2 to Bob. How many does she have?
Reasoning: Jane starts with 5 apples. She gives away 2, so 5 - 2 = 3.
Answer: 3

Example 2:
Question: There are 10 birds. 3 fly away. How many remain?
Reasoning: Start with 10 birds. 3 leave, so 10 - 3 = 7.
Answer: 7

Question: A store has 15 apples. They sell 3 each day. How many days until 6 remain?

Self-Consistency

Generate multiple solutions and pick the best:

Generate 5 different solutions to this problem. 
Then evaluate each and select the best one.

Problem: [Your problem]

Solution 1:
Solution 2:
Solution 3:
Solution 4:
Solution 5:

Evaluation:
Best Solution:

Tree of Thoughts

Explore multiple reasoning paths:

Solve this problem by exploring multiple approaches:

Problem: [Your problem]

Approach 1: [First approach]
  Reasoning:
  Result:

Approach 2: [Second approach]
  Reasoning:
  Result:

Approach 3: [Third approach]
  Reasoning:
  Result:

Compare approaches and select the best:

ReAct Pattern

Reasoning + Acting:

You are an AI agent solving problems. Use this format:

Thought: [Your reasoning about what to do]
Action: [Action to take]
Observation: [Result of action]
... (repeat Thought/Action/Observation)
Thought: [Final reasoning]
Answer: [Final answer]

Problem: [Your problem]

Few-Shot Learning

Provide examples in your prompt:

Classification Example

Classify these emails as spam or not spam:

Email: "Win $1000 now! Click here!"
Classification: spam

Email: "Meeting tomorrow at 3pm"
Classification: not spam

Email: "Your order has shipped"
Classification: not spam

Email: "Free money! Limited time offer!"
Classification: ?

Code Generation Example

Generate React components following these patterns:

Example 1:
Input: Button component with primary variant
Output:
```typescript
function Button({ children, variant = 'primary' }) {
  return (
    <button className={`btn btn-${variant}`}>
      {children}
    </button>
  );
}

Example 2: Input: Input component with label Output:

function Input({ label, ...props }) {
  return (
    <div>
      <label>{label}</label>
      <input {...props} />
    </div>
  );
}

Input: Card component with title and content Output:

## Role-Playing

Assign specific roles:

You are a senior software architect with 20 years of experience reviewing code for a React Native mobile app.

Your expertise includes:

• Performance optimization
• Security best practices
• Code maintainability
• React Native patterns

Review this code and provide feedback:

[Code here]

## Prompt Chaining

Break complex tasks into steps:

### Step 1: Planning

Analyze this task and create a step-by-step plan:

Task: [Your task]

Plan: 1. 2. 3.

### Step 2: Execution

Execute this step from the plan:

Step: [Step from plan] Context: [Previous results]

Execution:

### Step 3: Synthesis

Combine these results into a final answer:

Results:

• [Result 1]
• [Result 2]
• [Result 3]

Synthesis:

## Constraint-Based Prompting

Set explicit constraints:

Generate a function with these constraints:

Requirements:

• Function name: calculateTotal
• Parameters: items (array of {price: number, quantity: number})
• Return type: number
• Must handle empty arrays
• Must handle invalid inputs (return 0)
• Must be optimized for performance
• Must include JSDoc comments
• Must follow TypeScript best practices

Generate the function:

## Output Formatting

Specify exact output format:

Analyze this code and provide feedback in this format:

Performance Issues

• [Issue 1]
• [Issue 2]

Security Concerns

• [Concern 1]
• [Concern 2]

Code Quality

• [Quality issue 1]
• [Quality issue 2]

Recommendations

1. [Recommendation 1]
2. [Recommendation 2]

Code: [Code here]

## Iterative Refinement

Refine outputs through multiple passes:

### Pass 1: Initial Generation

Generate an initial solution:

Problem: [Your problem]

Initial Solution:

### Pass 2: Critique

Review this solution and identify issues:

Solution: [From Pass 1]

Issues:

### Pass 3: Refinement

Improve this solution based on the critique:

Original Solution: [From Pass 1] Issues: [From Pass 2]

Improved Solution:

## Meta-Prompting

Prompt the AI to create better prompts:

You are an expert at creating prompts for AI systems.

Create a prompt that will help an AI:

• [Goal 1]
• [Goal 2]
• [Goal 3]

The prompt should:

• Be clear and specific
• Include examples
• Set constraints
• Specify output format

Create the prompt:

## Advanced Patterns

### Constitutional AI

Have AI critique its own outputs:

Generate a solution, then critique it:

Problem: [Your problem]

Solution: [Generate solution]

Critique:

• What are potential issues?
• What could be improved?
• Are there edge cases not handled?

Revised Solution: [Improve based on critique]

### Reflection

Have AI reflect on its reasoning:

Solve this problem, then reflect on your approach:

Problem: [Your problem]

Solution: [Generate solution]

Reflection:

• Was my approach correct?
• What assumptions did I make?
• Could I have solved this differently?
• What did I learn?

## Prompt Templates

Create reusable templates:

```typescript
function createCodeReviewPrompt(code: string, language: string): string {
  return `
You are a senior ${language} developer reviewing code.

Review this code for:
- Performance issues
- Security vulnerabilities
- Best practices
- Maintainability

Code:
\`\`\`${language}
${code}
\`\`\`

Provide feedback in this format:
## Issues
- [List issues]

## Recommendations
- [List recommendations]
`;
}

Measuring Prompt Effectiveness

A/B Testing

async function testPrompts(prompts: string[], testCases: TestCase[]) {
  const results = prompts.map(async prompt => {
    const outputs = await Promise.all(
      testCases.map(testCase => generate(prompt, testCase))
    );
    
    return {
      prompt,
      accuracy: calculateAccuracy(outputs, testCases),
      avgTokens: calculateAvgTokens(outputs),
      avgLatency: calculateAvgLatency(outputs),
    };
  });
  
  return results;
}

Best Practices

1. Be specific: Clear, detailed instructions
2. Provide context: Relevant background information
3. Use examples: Show what you want
4. Set constraints: Define boundaries
5. Specify format: How should output be structured
6. Iterate: Refine based on results
7. Test: Validate with multiple inputs
8. Document: Keep track of what works

Common Mistakes

1. Too vague: "Make it better" → "Optimize for O(n) time complexity"
2. Missing context: Provide relevant background
3. No examples: Show desired output format
4. Ignoring constraints: Set clear boundaries
5. Not iterating: Refine prompts based on results

Conclusion

Advanced prompting techniques unlock AI capabilities:

• Chain-of-thought: Step-by-step reasoning
• Self-consistency: Multiple solutions
• Few-shot: Learning from examples
• Role-playing: Context-specific responses
• Iterative refinement: Improving outputs

Master these techniques, and you'll get dramatically better results from AI systems. The key is experimentation—try different approaches and measure what works best for your use case.