Technical Prompts for ChatGPT

Introduction

Technical prompts unlock ChatGPT's full potential for software development, testing, debugging, and system design. Whether you're generating production code, reviewing pull requests, or designing scalable architectures, the right prompting strategy makes all the difference.

This guide provides actionable patterns and best practices for crafting effective technical prompts that deliver precise, production-ready results.

Core Technical Categories

Code Development

Code Generation
Create production-ready code with proper error handling, type safety, and documentation. Learn how to specify requirements, constraints, and quality standards.

Code Refactoring
Transform legacy code into maintainable, efficient solutions. Master techniques for improving code quality while preserving functionality.

Code Review
Conduct thorough code reviews focusing on best practices, security, performance, and maintainability. Get actionable feedback with specific recommendations.

System Design & Architecture

Architecture Design
Design scalable, resilient systems with clear component diagrams, data flows, and technology stack recommendations.

Example prompt structure:

Design a [system type] that handles [scale/requirements]:

Requirements:
- Functional: [list key features]
- Non-functional: [performance, security, scalability]
- Constraints: [budget, timeline, existing systems]

Provide:
1. High-level architecture diagram
2. Component breakdown with responsibilities
3. Data flow and communication patterns
4. Technology stack with justification
5. Scalability and resilience strategies

Quality Assurance

Testing Scenarios
Generate comprehensive test cases covering edge cases, error conditions, and user flows. Create unit, integration, and end-to-end test strategies.

Debugging Techniques
Systematically diagnose and resolve issues with structured debugging prompts that identify root causes and provide solutions.

Example debugging prompt:

Debug this issue:

Error: [exact error message]

Context:
- Expected behavior: [what should happen]
- Current behavior: [what actually happens]
- Environment: [OS, runtime version, dependencies]
- Recent changes: [what changed before the issue]

Steps to reproduce:
1. [step 1]
2. [step 2]
3. [step 3]

Provide:
1. Root cause analysis
2. Step-by-step solution
3. Prevention strategies

Performance & Optimization

Performance Analysis
Identify bottlenecks, analyze metrics, and optimize system performance with data-driven recommendations.

Optimization Techniques
Apply proven optimization patterns for code, database queries, API calls, and resource utilization.

Documentation

Documentation Writing
Create clear, comprehensive technical documentation including API references, user guides, and architecture decision records.

Best Practices for Technical Prompts

1. Be Specific and Contextual

Vague prompts produce generic results. Provide concrete details:

• Technology stack: Language versions, frameworks, libraries
• Constraints: Performance requirements, resource limits, compatibility needs
• Environment: Development, staging, production configurations
• Scope: What's in scope and explicitly out of scope

Example:

❌ Vague: "Create a login function"

✓ Specific: "Create a secure login function in TypeScript using bcrypt 
for password hashing, JWT for session management, and Express middleware. 
Include rate limiting (5 attempts per 15 minutes), input validation, 
and proper error handling for invalid credentials."

2. Structure Your Prompts

Use clear formatting for complex requests:

• Numbered lists for sequential steps
• Bullet points for requirements
• Code blocks for examples
• Sections for different aspects (requirements, constraints, output format)

3. Request Specific Output Formats

Define exactly what you want to receive:

Provide the solution as:
1. Code implementation with inline comments
2. Unit tests covering edge cases
3. Usage examples
4. Performance considerations
5. Security review checklist

4. Include Examples

Show examples of what you want (or don't want):

Generate API documentation similar to this structure:
[example documentation]

Avoid generic descriptions. Include:
- Request/response examples
- Error codes and handling
- Rate limits and authentication
- Common use cases

Advanced Prompting Techniques

Chain of Thought Reasoning

For complex problems, request step-by-step analysis:

Analyze this [problem/code/architecture] using chain of thought:

1. Initial assessment: What are the key components and relationships?
2. Identify issues: What problems or inefficiencies exist?
3. Evaluate alternatives: What are 2-3 different approaches?
4. Compare trade-offs: Pros/cons of each approach
5. Recommend solution: Best approach with justification
6. Implementation plan: Step-by-step execution strategy

Expert Role Prompting

Leverage domain expertise:

Act as a senior DevOps engineer with 10+ years of Kubernetes experience.

Review this deployment configuration for:
- Security best practices (least privilege, secrets management)
- Resource optimization (requests/limits, autoscaling)
- High availability (pod disruption budgets, anti-affinity)
- Monitoring and observability (health checks, metrics)

Provide specific recommendations with YAML examples.

Iterative Refinement

Start broad, then narrow focus:

1. Initial prompt: High-level requirements
2. Review response: Identify gaps or unclear areas
3. Refine prompt: Add specific constraints or examples
4. Validate: Test the solution and iterate

Common Pitfalls to Avoid

1. Vague Requirements

Problem: "Make this code better"
Solution: "Refactor this code to improve: (1) time complexity from O(n²) to O(n log n), (2) memory usage by using streaming, (3) readability with descriptive variable names"

2. Over-Complexity

Problem: Requesting 10 different features in one prompt
Solution: Break into focused prompts, tackle one concern at a time

3. Missing Context

Problem: Sharing code without explaining the broader system
Solution: Provide context about how this component fits into the larger architecture, what calls it, what it depends on

4. Ignoring Constraints

Problem: Not specifying performance, security, or compatibility requirements
Solution: Explicitly state all constraints upfront

Tips for Production-Ready Results

Validate Assumptions

Always state and confirm:

• Prerequisites and dependencies
• Expected input/output formats
• Error handling requirements
• Performance benchmarks

Request Testing Strategies

Don't just ask for code—ask for:

• Unit tests with edge cases
• Integration test scenarios
• Performance test criteria
• Security test cases

Document and Iterate

Build a personal prompt library:

• Save prompts that worked well
• Note what didn't work and why
• Refine patterns over time
• Share successful patterns with your team

Related Resources

Explore specialized guides for deeper dives into specific technical areas:

• Algorithm Design: Learn how to prompt for efficient algorithms and data structures
• Security Best Practices: Prompts for security reviews, threat modeling, and vulnerability assessment
• Database Optimization: Query optimization, schema design, and indexing strategies

Conclusion

Effective technical prompting is a skill that improves with practice. Start with clear, specific requirements, provide relevant context, and structure your prompts for the output you need. Use the patterns and examples in this guide as templates, then adapt them to your specific use cases.

The specialized guides linked throughout this page provide deeper coverage of each technical domain, complete with ready-to-use prompt templates and real-world examples.