Comprehensive guide for using OpenAI GPT-5 series models with the Tour Operator WordPress plugin.
- ← Back to README
- AGENTS.md - AI operations system overview
- Custom Instructions - Copilot configuration
- Instructions - Coding standards
- Prompts - Task templates
- Agents - Specialized agents
- Chat Modes - Interactive workflows
- CLAUDE.md - Claude (Anthropic) optimization
- GEMINI.md - Gemini (Google) optimization
- WordPress Packages - Package reference
This document provides GPT-5 series optimization strategies for Tour Operator development. GPT-5 excels at complex reasoning, multi-step planning, and architectural decisions.
- Best for: Deep reasoning, multi-step problem solving, architecture-level analysis
- Context window: 128K tokens
- Premium multiplier: 1x
- Capabilities: Agent mode
- Status: Public Preview
- Use cases:
- Complex architectural decisions
- Performance optimization
- Security audits
- Multi-file refactoring
- Best for: Complex reasoning, code analysis, technical decision-making
- Context window: 128K tokens
- Premium multiplier: 1x
- Capabilities: Reasoning
- Use cases:
- Complex multi-step problem solving
- Complex algorithm design
- Strategic refactoring plans
- Performance optimization strategies
- Best for: Fast, accurate code completions and explanations
- Context window: 128K tokens
- Premium multiplier: 0x (included)
- Capabilities: Agent mode, Reasoning, Vision
- Use cases:
- Quick bug fixes
- Simple code generation
- Inline suggestions
Use GPT-5 for planning:
Design the architecture for a booking system that includes:
- Tour selection with date picker
- Pricing calculation with seasonal rates
- Multi-step booking form
- Payment integration
- Booking confirmation
Provide:
1. Component breakdown
2. Data flow architecture
3. State management strategy
4. Integration points
5. Testing strategy
Use GPT-5-Codex for implementation:
Based on the architecture, implement the booking-form block:
- Use @wordpress/components
- Integrate with tour pricing API
- Handle validation
- Support theme.json styling
- Include accessibility features
Use o1 for analysis:
Analyze tour listing performance issues:
- Current load time: 3.2s
- Database queries: 47 per page
- Largest contentful paint: 2.8s
Provide:
1. Performance bottleneck analysis
2. Optimization strategy
3. Implementation priorities
4. Expected improvements
Use GPT-5-Codex for implementation:
Implement caching strategy for tour listings:
- Use WordPress transient API
- Cache taxonomy queries
- Optimize meta queries
- Implement lazy loading
Use GPT-5-Codex with full context:
Refactor tour booking system across:
- includes/classes/class-tour-booking.php
- includes/classes/class-tour-pricing.php
- includes/classes/class-booking-validation.php
- src/blocks/booking-form/index.js
Goals:
- Consolidate duplicate code
- Improve error handling
- Add comprehensive validation
- Enhance accessibility
| Prompt | Model | Why GPT | Complexity |
|---|---|---|---|
| block-architect | GPT-5 | Strategic architecture | Very High |
| refactor-plan | GPT-5 | Multi-step planning | High |
| performance-audit | GPT-5-Codex | Deep analysis | High |
| security-audit | GPT-5-Codex | Critical accuracy | High |
| code-review | GPT-5-Codex | Comprehensive review | Medium |
| bugfix-risk | GPT-5-Codex | Risk analysis | Medium |
| Agent | Model | Why GPT | Output |
|---|---|---|---|
| block-architect | GPT-5 | Complex architecture | Architecture docs |
| code-governor | GPT-5-Codex | Quality enforcement | Quality report |
| block_audit | GPT-5-Codex | Comprehensive audit | Audit report |
| Mode | Model | Why GPT | Workflow |
|---|---|---|---|
| block-plugin-developer | GPT-5-Codex | Complete plugin dev | 8-12 turns |
| refactor | GPT-5 | Complex refactoring | 6-10 turns |
| review | GPT-5-Codex | Thorough review | 4-6 turns |
| reviewer | GPT-5-Codex | Deep dive | 8-12 turns |
For architectural decisions:
1. Explain the problem completely
2. Include constraints and requirements
3. Request step-by-step reasoning
4. Ask for alternatives with trade-offs
Provide response in this format:
## Problem Analysis
[Analysis of current state]
## Proposed Solution
[Detailed solution]
## Implementation Steps
1. Step 1
2. Step 2
## Testing Strategy
[How to validate]
## Potential Issues
[Edge cases and risks]
Include all related files in context:
Context files:
- src/blocks/tour-card/index.js
- src/blocks/tour-card/edit.js
- src/blocks/tour-card/block.json
- includes/blocks/class-tour-card.php
Task: Refactor for performance
1. Initial: "Design booking form architecture"
2. Refine: "Add payment gateway integration"
3. Optimize: "Improve validation error handling"
4. Finalize: "Add comprehensive tests"
Use GPT-5 for planning:
Design a tour search block with:
- Location-based filtering
- Date range picker
- Price range slider
- Tour type taxonomies
- Real-time search results
Requirements:
- Server-side rendering for SEO
- Client-side filtering for UX
- Accessible keyboard navigation
- Mobile-responsive design
- Performance: <100ms filter updates
Provide:
1. Component architecture
2. State management approach
3. API integration strategy
4. Accessibility considerations
5. Performance optimization plan
Use GPT-5-Codex for analysis:
Audit booking system security:
- Payment processing
- User data handling
- Admin capabilities
- AJAX endpoints
- Nonce verification
Check for:
- SQL injection vulnerabilities
- XSS vulnerabilities
- CSRF protection
- Data sanitization
- Authorization checks
Provide:
1. Security issues found (with severity)
2. Exploitation scenarios
3. Fix recommendations
4. Testing checklist
Use GPT-5 for strategy:
Optimize tour listing performance:
Current metrics:
- Load time: 3.2s
- TTFB: 800ms
- Database queries: 47
- Memory usage: 125MB
Goal metrics:
- Load time: <1.5s
- TTFB: <200ms
- Database queries: <15
- Memory usage: <64MB
Provide:
1. Performance analysis
2. Optimization strategy
3. Implementation priorities
4. Expected improvements
5. Monitoring approach
# Use GPT-5 mini for inline suggestions
# Copilot automatically uses GPT models for:
- Code completion
- Inline chat
- Quick fixes
# Specify model for complex tasks
@workspace /gpt-5 Refactor this booking system
# Architecture planning with GPT-5
@workspace /gpt-5 Design the booking system architecture
# Implementation with GPT-5-Codex
@workspace /gpt Implement the booking form component
# Quick fixes with Mini
@workspace /gpt-5-mini Fix this validation error
✅ Do:
- Provide complete context upfront
- Ask for step-by-step reasoning
- Request alternatives and trade-offs
- Include constraints and requirements
❌ Don't:
- Rush the response (deep reasoning takes time)
- Provide partial context
- Ask for quick iterations
✅ Do:
- Use structured output formats
- Request code with comments
- Ask for complete implementations
- Include test requirements
❌ Don't:
- Assume context from previous unrelated chats
- Request creative variations (use GPT-5)
- Expect deep reasoning (use GPT-5)
✅ Do:
- Use for quick bug fixes
- Request simple code snippets
- Ask for straightforward explanations
❌ Don't:
- Use for complex architecture
- Expect comprehensive analysis
- Request multi-file refactoring
Phase 1: Architecture (GPT-5)
- Design system architecture
- Plan component structure
- Define API contracts
Phase 2: Implementation (GPT-5-Codex)
- Scaffold components
- Implement business logic
- Add error handling
Phase 3: Optimization (GPT-5-Codex)
- Performance tuning
- Security hardening
- Accessibility improvements
Phase 4: Testing (GPT-5 mini)
- Generate unit tests
- Create E2E tests
- Write test documentation
Step 1: Analysis (GPT-5-Codex)
@workspace /gpt Analyze this bug:
- Error message: [paste error]
- Relevant code: [paste code]
- Steps to reproduce: [list steps]
Step 2: Root Cause (GPT-5 for complex bugs)
@workspace /gpt-5 Determine root cause considering:
- Database state
- User permissions
- Cache interactions
- Third-party integrations
Step 3: Fix Implementation (GPT-5-Codex)
@workspace /gpt Implement fix with:
- Code changes
- Tests
- Documentation
Step 1: Automated Review (GPT-5-Codex)
@workspace /gpt Review this PR for:
- Code quality
- Security issues
- Performance concerns
- Accessibility compliance
Step 2: Architectural Review (GPT-5 for complex changes)
@workspace /gpt-5 Review architectural implications:
- System design impact
- Scalability concerns
- Maintenance burden
- Migration strategy
- Use GPT-5 mini for simple tasks
- Provide focused context (not entire codebase)
- Use specific prompts
- Request code-only responses when appropriate
- Use GPT-5 for complex reasoning
- Use GPT-5-Codex for production code
- Provide complete context
- Include relevant files
- Reference WordPress standards
- Validate with tests
- Use GPT-5 mini for development iterations (0x multiplier)
- Use GPT-5-Codex for production code
- Use o1 only for complex planning
- Cache common responses
- Minimize context size
GPT-5.1 (Preview):
- Slower responses (deep reasoning takes time)
- Best for planning, not rapid implementation
- 1x premium multiplier
GPT-5:
- Context limit: 128K tokens
- Best for complex reasoning and debugging
- Doesn't support Agent mode
GPT-5-Codex:
- Optimized for code, may be less creative
- 1x premium multiplier
- Great for complex engineering tasks
GPT-5 mini:
- Less capable with complex tasks
- May produce simpler solutions
- Best for straightforward problems
- 0x multiplier (free!)
- Always review generated code
- Test thoroughly before committing
- Verify WordPress standards compliance
- Check security implications
- Validate accessibility
- OpenAI Platform
- GPT-5 Documentation
- GitHub Copilot AI Models
- AI Model Comparison
- Tour Operator Docs
- WordPress Block Editor
| Feature | GPT-5.1 | GPT-5 | GPT-5 mini | Gemini 2.5 Pro | Claude Sonnet 4.5 |
|---|---|---|---|---|---|
| Reasoning | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| Speed | ⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ |
| Context | 128K | 128K | 128K | 1M | 200K |
| Multiplier | 1x | 1x | 0x | 1x | 1x |
| Code Quality | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| Architecture | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ |
| Scaffolding | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ |
| Agent Mode | ✅ | ❌ | ✅ | ❌ | ✅ |
| Vision | ❌ | ❌ | ✅ | ✅ | ❌ |
✅ Complex Reasoning (GPT-5/5.1)
- Architectural decisions
- Strategic planning
- Algorithm design
- System design
✅ Production Code (GPT-5-Codex)
- Complete implementations
- Security-critical features
- Performance optimization
- Multi-file refactoring
✅ Quick Tasks (GPT-5 mini)
- Bug fixes
- Simple features
- Code formatting
- Documentation updates
✅ Speed & Context
- Massive context (1-2M tokens)
- Rapid prototyping
- Batch operations
- Quick scaffolding
✅ Accuracy & Analysis
- Accessibility audits
- Detailed reviews
- Code quality analysis
- Long-form documentation
Start here:
- Simple task → GPT-5 mini (0x multiplier!)
- Standard implementation → GPT-5
- Complex planning → GPT-5.1
- Code-optimized → GPT-5-Codex
- Need speed → Gemini 2.5 Pro
- Need accuracy → Claude Sonnet 4.5
"GPT-5 is too slow" → Use GPT-5 mini for implementation tasks
"Need more complete code" → Request explicit completeness, or use GPT-5-Codex
"Need larger context" → Use Gemini 2.5 Pro (1M tokens) or Gemini 3 Pro (2M tokens)
"Need better accessibility analysis" → Use Claude Sonnet 4.5 or Opus 4.1
"Need faster iterations" → Use Gemini 2.5 Pro or Claude Haiku 4.5
Maximize Speed:
- Use GPT-5 mini for development (0x multiplier)
- Use GPT-5-Codex for production
- Minimize context size
- Request code-only responses
Maximize Quality:
- Use GPT-5.1 for planning
- Use GPT-5-Codex for implementation
- Provide complete context
- Include test requirements
- Reference WordPress standards
Balance Cost:
- Use GPT-5 mini when possible (0x multiplier)
- Use GPT-5-Codex for most tasks (1x)
- Reserve GPT-5.1 for complex decisions
- Cache common responses