Advanced Techniques for Power Users
Once you've mastered the core techniques, these advanced approaches unlock new levels of AI capability. They're more complex to implement but can solve problems that basic prompting can't touch.
Chain-of-Thought (CoT) Prompting: Teaching AI to Think
Chain-of-Thought prompting is perhaps the most important breakthrough in prompt engineering. Instead of asking for just an answer, you ask the AI to show its reasoning process step-by-step.
This technique dramatically improves performance on tasks requiring logic, math, or complex reasoning. It works because it forces the AI to break down problems into manageable pieces.
Basic Chain-of-Thought example:
I started with 8 marbles. I gave 3 to a friend, then found 4 more. How many marbles do I have now? Think step by step.
AI Response: Let me work through this step by step:
- Started with 8 marbles
- Gave away 3 marbles: 8 - 3 = 5 marbles remaining
- Found 4 more marbles: 5 + 4 = 9 marbles
Therefore, I now have 9 marbles.
Zero-shot Chain-of-Thought:
The simplest version just adds "Let's think step by step" or "Think through this carefully" to your prompt. This often works surprisingly well for reasoning tasks.
Few-shot Chain-of-Thought:
More powerful is showing the AI examples of step-by-step reasoning:
Q: A cafeteria had 23 apples. If they used 20 to make lunch and bought 6 more, how many apples do they have? A: The cafeteria started with 23 apples. They used 20 for lunch, so they had 23 - 20 = 3 apples left. Then they bought 6 more, so they have 3 + 6 = 9 apples now.
Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now? A: [Let the AI complete this with step-by-step reasoning]
When to use Chain-of-Thought:
- Mathematical problems
- Logical reasoning tasks
- Complex analysis requires multiple steps
- Any time you need to verify the AI's reasoning process
Self-Consistency Prompting: Multiple Paths to Truth
Self-consistency takes Chain-of-Thought further by generating multiple reasoning paths and selecting the most consistent answer. This technique significantly improves accuracy on complex problems.
Instead of relying on a single reasoning chain that might contain errors, self-consistency generates several different approaches to the same problem and chooses the answer that appears most frequently.
Example problem: "When I was 6, my sister was half my age. Now I'm 70. How old is my sister?"
Single reasoning path (often wrong): "If your sister was half your age when you were 6, she was 3. Now you're 70, so she's 35."
Self-consistency approach:
- Path 1: "When you were 6, your sister was 3. The age difference is 3 years. Now you're 70, so she's 67."
- Path 2: "Your sister is 3 years younger than you. If you're 70, she's 67."
- Path 3: "At 6, sister was 3. Age gap = 3 years. Current age = 70 - 3 = 67."
All paths lead to 67, so that's the answer.
Implementing self-consistency:
You can implement this manually by asking for multiple reasoning approaches:
Solve this problem using three different reasoning methods: [Problem statement]
Method 1: [Approach 1] Method 2: [Approach 2]Method 3: [Approach 3]
Compare your answers and explain which is most likely correct.
Meta Prompting: Abstract Guidance
Meta prompting focuses on the structure and logic of responses rather than specific content. Instead of providing detailed examples, you give abstract guidance that the AI can apply across different scenarios.
Traditional few-shot approach:
Example 1: [Specific math problem with solution] Example 2: [Another specific math problem with solution] Now solve: [New math problem]
Meta prompting approach:
When solving math problems, follow this structure:
Step 1: Identify the variables and what you're solving for
Step 2: Determine which mathematical operations are needed
Step 3: Perform the calculations in the correct order
Step 4: Check your answer for reasonableness.
Now solve: [Math problem]
Benefits of meta prompting: Meta prompting uses fewer tokens than multiple examples. It reduces bias from specific cases and generalises better to new scenarios. You get a consistent structure across different inputs.
Best practices: Focus on logical structure rather than content. Keep instructions abstract but clear. Define the format and approach, not the specifics. Use this technique when you need a consistent methodology across varied inputs.
AI Quality Control Using Self-Criticism and Reflection
Self-criticism techniques ask the AI to evaluate and improve its own output. This can catch errors, improve quality, and provide more thoughtful responses.
Basic self-criticism:
First, provide your initial response to this question: [Question]
Now, critically evaluate your response. What are the potential weaknesses or errors? How could it be improved?
Finally, a revised response will be provided that addresses these issues.
Structured self-reflection:
Answer this question: [Question]
Now evaluate your answer using these criteria:
- Accuracy: Are the facts correct?
- Completeness: Did you address all parts of the question?
- Clarity: Is the explanation easy to understand?
- Relevance: Does everything relate to the question asked?
Provide a score (1-10) for each criterion and explain your reasoning. Then give an improved answer.
Breaking Down Complexity with Prompt Chaining
Prompt chaining breaks complex tasks into a sequence of simpler prompts, where each prompt builds on the output of the previous one. This is essential for complex workflows that exceed what a single prompt can handle.
Example: Content creation workflow:
Prompt 1: "Analyze this topic and identify the 5 most important subtopics to cover: [Topic]"
Prompt 2: "For each subtopic from the previous analysis, create a detailed outline with 3-4 key points: [Subtopics from Prompt 1]"
Prompt 3: "Write a comprehensive section on the first subtopic using this outline: [First subtopic outline from Prompt 2]"
Prompt 4: "Review the section you just wrote and suggest 3 specific improvements for clarity and engagement."
Benefits of prompt chaining:
- Handles complex tasks that single prompts can't manage
- Allows for quality control at each step
- Enables iterative improvement
- Breaks down overwhelming tasks into manageable pieces
Constrain Format and Length: Precision Control
Advanced format control goes beyond basic structure to specify exact output requirements. This is crucial for production systems where consistency matters.
Basic format constraint: "Respond in JSON format with these fields: name, category, priority."
Advanced format constraint:
Respond using this exact JSON structure. Do not include any explanation or additional text:
{ "analysis": { "main_issue": "string (max 50 characters)", "severity": "low|medium|high", "confidence": "number (0-100)" }, "recommendations": [ { "action": "string (max 100 characters)", "timeline": "immediate|short-term|long-term", "effort": "low|medium|high" } ] }
Model-specific formatting tips:
ChatGPTo: Responds well to markdown-like syntax and clear delimiters. Use triple backticks for code blocks and ### for headers.
Claude: Benefits from explicit structural scaffolding. Use XML-like tags: <analysis>, <recommendation>, etc.
Gemini Pro: Excels with hierarchical formatting. Define structure at the beginning and use numbered lists and clear headings.
Combining Advanced Techniques
The most powerful prompts often combine multiple advanced techniques:
Chain-of-Thought + Self-Consistency + Format Control:
Solve this problem using step-by-step reasoning. Provide three different solution approaches, then determine which is most reliable.
Format your response as:
Approach 1
[Step-by-step reasoning]
Approach 2
[Alternative reasoning]
Approach 3
[Third approach]
Final Answer
[Most reliable solution with justification]
Problem: [Complex problem requiring reasoning]
When to Use Advanced Techniques
Advanced techniques are powerful but come with costs:
- They use more tokens (higher costs)
- They take longer to execute
- They're more complex to implement and debug
Use them when:
- Basic techniques aren't sufficient
- You need high reliability and accuracy
- The task involves complex reasoning or analysis
- You're building production systems
- The cost of errors is high
Practice with Advanced Techniques
Try implementing these techniques with a complex problem from your work:
- Start with a basic prompt
- Add Chain-of-Thought reasoning
- Implement self-criticism
- Add format constraints
- Compare the quality and reliability of results
You'll likely find that advanced techniques produce more thoughtful, accurate, and useful outputs, even if they require more effort to implement.