Advanced Prompt Engineering Techniques

Move beyond basics with these professional-grade prompt engineering techniques used by AI researchers and practitioners.

Chain-of-Thought Prompting

What It Is

Asking the model to show its reasoning process before providing an answer.

When to Use

• Complex reasoning tasks
• Mathematical problems
• Multi-step analysis
• Logical deductions

Implementation

"Let's think through this step by step:

1. First, identify the key factors
2. Then, analyze how they interact
3. Finally, draw your conclusion

Now apply this process to: [your question]"

Variations

• Zero-shot CoT: Simply add "Let's think step by step"
• Few-shot CoT: Provide examples with reasoning chains
• Self-consistency: Generate multiple reasoning paths and take majority answer

Few-Shot Learning

What It Is

Providing examples that demonstrate the desired input-output pattern.

Best Practices

• Use 3-5 diverse examples
• Cover edge cases in examples
• Ensure examples are high quality
• Match the complexity of target task

Template

"Here are some examples:

Input: [example 1 input] Output: [example 1 output]

Input: [example 2 input] Output: [example 2 output]

Now complete this: Input: [actual input] Output:"

Role-Based Prompting

Beyond Simple Roles

Create complex personas with:

• Professional background
• Communication style
• Knowledge boundaries
• Decision-making frameworks

Example

"You are a CFO with 20 years of experience in tech startups. You're known for:

• Conservative financial projections
• Clear communication with non-financial stakeholders
• Focus on unit economics and path to profitability
• Skepticism of vanity metrics

Evaluate this business plan..."

Structured Output Techniques

JSON Output

"Respond with a JSON object containing:

• summary: string (max 100 words)
• key_points: array of strings
• sentiment: 'positive' | 'negative' | 'neutral'
• confidence: number between 0 and 1"

XML/Custom Formats

Define precise structures for consistent parsing.

Meta-Prompting

Self-Improvement

"Review your previous response. Identify any weaknesses or gaps. Then provide an improved version that addresses these issues."

Prompt Generation

"Create a prompt that would generate [desired output type]. The prompt should be detailed enough to produce consistent results."

Temperature and Parameter Optimization

Temperature Guide

• 0.0-0.3: Factual, deterministic tasks
• 0.3-0.7: Balanced creativity and consistency
• 0.7-1.0: Creative, varied outputs

Other Parameters

• Top-p: Controls diversity of token selection
• Frequency penalty: Reduces repetition
• Presence penalty: Encourages topic diversity

Testing and Iteration

Systematic Testing

1. Define success criteria
2. Create test cases covering various scenarios
3. Run tests and measure results
4. Identify failure patterns
5. Refine prompt to address failures
6. Repeat

A/B Testing

Compare prompt variations:

• Different phrasings
• Various example sets
• Alternative structures

Track metrics:

• Accuracy
• Consistency
• User satisfaction
• Task completion rate

Advanced prompt engineering is about systematic optimization, not guesswork.