This advice came from Claude at Anthropic.

The bottom line is that the more structure the user provides, the better the results.

Advanced Prompting Techniques Guide

This guide covers proven techniques to get significantly better results from AI language models through structured prompting.

Core Principle

The more structure you provide in your prompt, the more likely you’ll get the response you’re actually looking for.

1. Reasoning Tags

<scratchpad> - Show Your Work

Forces the AI to think step-by-step before providing final answers. Results in more thorough analysis and fewer errors.

When to use:

  • Complex problem-solving
  • Multi-step calculations
  • Architectural decisions
  • Debugging issues
  • Comparing multiple approaches

Example:

Analyze the best database solution for our e-commerce platform. 
Use <scratchpad> to evaluate the options before making a recommendation.

Result: The AI will work through considerations like scalability, cost, team expertise, and performance before concluding.

<thinking> or <analysis>

Similar to scratchpad but for broader analytical processes.

Example:

Evaluate our marketing strategy. Use <thinking> tags to analyze 
market conditions, competitor positioning, and our unique advantages.

2. Structural Tags

Content Organization

  • <overview> - High-level summary
  • <details> - In-depth information
  • <examples> - Practical use cases
  • <summary> - Key takeaways

Comparative Analysis

  • <pros> and <cons> - Balanced evaluation
  • <option1>, <option2>, etc. - Compare alternatives
  • <recommendation> - Final decision with reasoning

Technical Documentation

  • <requirements> - What’s needed
  • <implementation> - How to build it
  • <testing> - How to verify it works
  • <deployment> - How to release it

Code-Specific Tags

  • <code> - Executable code blocks
  • <explanation> - How the code works
  • <security_considerations> - Security implications
  • <performance_notes> - Optimization details

3. Custom Tags

You can create your own tags for specific needs. The AI will structure responses accordingly.

Business Analysis Example:

Analyze this startup idea using:
<market_opportunity>
<competitive_landscape>
<risk_factors>
<revenue_potential>
<next_steps>

Code Review Example:

Review this code using:
<functionality_assessment>
<security_issues>
<performance_concerns>
<maintainability>
<recommended_changes>

4. Few-Shot Prompting

Provide 2-3 examples of the input/output pattern you want.

Template:

Here are examples of the format I want:

Example 1:
Input: [sample input]
Output: [desired response format]

Example 2:
Input: [sample input]  
Output: [desired response format]

Now apply this same format to: [your actual request]

Coding Example:

Examples of the code review format I want:

Input: function calculateTotal(items) { return items.reduce((sum, item) => sum + item.price, 0); }
Output: 
✅ **Functionality**: Correctly sums item prices
⚠️ **Edge Cases**: Should handle empty arrays and null values
💡 **Suggestion**: Add input validation

Now review this code: [your actual code]

5. Role/Persona Prompting

Set the AI’s perspective and expertise level.

Examples:

  • “Act as a senior DevOps engineer with 10 years of Kubernetes experience…”
  • “Respond as a security consultant reviewing this architecture…”
  • “Take the perspective of a startup CTO making technology decisions…”

Why it works: Different roles emphasize different priorities and have different knowledge depths.

6. Constraint-Based Prompting

Explicitly define limitations and requirements.

Template:

<constraints>
- Budget: Under $5,000
- Timeline: 2 weeks maximum
- Technology: Must use Python 3.8+
- Team: 2 junior developers
- Compatibility: Works with existing MySQL database
</constraints>

7. Output Format Templates

Specify exactly how you want the response structured.

Example:

Please respond using this exact format:

## Problem Analysis
[Your analysis here]

## Proposed Solution
[Your solution here]  

## Implementation Steps
1. [Step 1]
2. [Step 2]
3. [Step 3]

## Risks & Mitigations
- **Risk:** [description] **Mitigation:** [solution]

## Success Metrics
[How we'll measure success]

8. Multi-Step Instructions

Break complex requests into sequential steps.

Example:

Step 1: First, analyze the current system architecture
Step 2: If you identify any critical issues, highlight them before proceeding
Step 3: Then propose specific improvements
Step 4: Finally, prioritize improvements by impact and effort

9. Negative Constraints

Tell the AI what NOT to include.

Example:

Do NOT provide:
- Generic tutorials or theory
- Deprecated solutions
- Solutions requiring additional paid services

DO provide:
- Specific code for our exact use case  
- Modern best practices
- Free/open-source solutions only

10. Iterative Prompting

Use tags to request drafts and refinements.

Example:

Create a project plan for our mobile app.

First, provide a <draft> version focusing on major milestones.
Then, create a <detailed> version with weekly breakdown.
Finally, give me a <executive_summary> for stakeholders.

Combining Techniques

The most effective prompts often combine multiple techniques:

Example:

Act as a senior software architect.

<constraints>
- Legacy PHP system that can't be replaced immediately
- Team of 3 developers
- 6-month timeline
- Must maintain 99.9% uptime during migration
</constraints>

Use <scratchpad> to analyze migration strategies, then provide:

<recommendation>
[Your recommended approach]
</recommendation>

<implementation_phases>
Phase 1: [details]
Phase 2: [details]  
Phase 3: [details]
</implementation_phases>

<risk_mitigation>
[How to handle potential issues]
</risk_mitigation>

Best Practices

  1. Start Simple: Begin with basic structure, add complexity as needed
  2. Be Specific: Vague tags get vague results
  3. Match Complexity: Use advanced techniques for complex problems
  4. Iterate: Refine your prompting approach based on results
  5. Document Patterns: Save effective prompt templates for reuse

When NOT to Use Advanced Prompting

Skip the complexity for:

  • Simple factual questions
  • Basic syntax help
  • Quick clarifications
  • Casual conversations

Testing Your Prompts

Try the same question with and without structure to see the difference:

Basic: “How do I optimize this database query?”

Structured:

Act as a database performance expert.

<current_situation>
- PostgreSQL 13
- Query runs in 2.3 seconds
- Table has 50M records
- Query: SELECT * FROM orders WHERE created_at > '2023-01-01' AND status = 'pending'
</current_situation>

Use <scratchpad> to analyze performance bottlenecks, then provide:
<optimization_steps>
<expected_performance_gain>
<monitoring_recommendations>

The structured version will typically produce more actionable, specific advice.

Remember: These techniques work across different AI models (Claude, ChatGPT, Gemini) but may require slight adjustments based on the specific model’s strengths.

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