AI Systems Prompting Guides - Complete Collection
Overview
This collection provides comprehensive prompting guides for six major AI systems, each tailored to their unique capabilities, interfaces, and use cases. The guides follow a consistent structure while highlighting the distinctive features and optimal prompting strategies for each system.
Guide Collection Summary
Chat-Based AI Systems
1. ChatGPT Prompting Guide
Focus: Conversational AI with strong reasoning and multimodal capabilities Key Strengths:
- Custom instructions for persistent preferences
- Multimodal processing (text, images, audio)
- Code interpreter and data analysis
- Web browsing capabilities
- Extensive plugin ecosystem
Best For: General-purpose tasks, content creation, data analysis, coding assistance, research
Unique Prompting Approaches:
- Role-playing and persona adoption
- Chain-of-thought reasoning
- Few-shot prompting with examples
- Iterative refinement through conversation
- Custom instructions for consistent behavior
2. Gemini Prompting Guide
Focus: Multimodal AI with massive context window and Google ecosystem integration Key Strengths:
- 1M+ token context window for large documents/codebases
- Superior multimodal reasoning
- Built-in Google Search integration
- Code execution capabilities
- Strong analytical and research capabilities
Best For: Large-scale analysis, comprehensive research, multimodal tasks, complex reasoning
Unique Prompting Approaches:
- Long-context utilization for entire codebases
- Search-grounded prompting for current information
- Multimodal chain-of-thought reasoning
- Structured analytical frameworks
- Comprehensive context provision
3. Manus AI Prompting Guide
Focus: Autonomous AI agent that executes complete tasks and delivers finished results Key Strengths:
- End-to-end task execution
- Multi-deliverable project completion
- Autonomous workflow management
- Professional-quality outputs
- Deployment-ready solutions
Best For: Complete project delivery, business automation, comprehensive solutions, professional deliverables
Unique Prompting Approaches:
- Outcome-focused prompting (not conversational)
- Comprehensive context provision upfront
- Multi-deliverable project requests
- Production-ready specification
- Autonomous execution trust
Command-Line Interface (CLI) AI Systems
4. Claude Code CLI Prompting Guide
Focus: Agentic coding assistant integrated with development environment Key Strengths:
- Automatic codebase context gathering
- Direct file editing and manipulation
- Git integration and version control
- Web search for documentation
- Enterprise platform integration
Best For: Code development, debugging, refactoring, project analysis
Unique CLI Approaches:
- Concise, terminal-optimized responses
- Action-oriented prompting
- Codebase-aware requests
- Direct file modification commands
- Integration with development workflow
5. OpenAI Codex CLI Prompting Guide
Focus: Lightweight coding agent with granular autonomy control and safety features Key Strengths:
- Three-tier approval system (Suggest/Auto Edit/Full Auto)
- Sandboxed execution environment
- Version control integration
- Multimodal input support
- Safety-first design
Best For: Safe code experimentation, controlled automation, learning and exploration
Unique CLI Approaches:
- Approval mode specification
- Safety-conscious prompting
- Progressive autonomy requests
- Constraint-driven development
- Sandboxed experimentation
6. Gemini CLI Prompting Guide
Focus: Large-scale development with Google ecosystem integration and real-time search Key Strengths:
- Massive context window for large codebases
- Built-in Google Search integration
- MCP server connectivity
- Media generation capabilities
- Google ecosystem integration
Best For: Large-scale projects, research-driven development, ecosystem integration
Unique CLI Approaches:
- Large-scale analysis prompting
- Search-integrated development
- Multi-phase project prompting
- Ecosystem integration requests
- Research-driven implementation
Key Differences and Selection Criteria
Chat vs CLI Interfaces
Chat Interfaces (ChatGPT, Gemini, Manus AI):
- Conversational interaction style
- Iterative refinement through dialogue
- Comprehensive explanations and context
- Multi-turn problem solving
- Rich formatting and presentation
CLI Interfaces (Claude Code CLI, Codex CLI, Gemini CLI):
- Direct, action-oriented commands
- Terminal-optimized responses
- File system integration
- Development workflow integration
- Concise, executable outputs
Autonomy Levels
High Autonomy: Manus AI, Gemini CLI (Full Auto), Codex CLI (Full Auto)
- Complete task execution
- Minimal user intervention
- End-to-end delivery
- Production-ready outputs
Medium Autonomy: Claude Code CLI, Gemini (with search), ChatGPT (with plugins)
- Guided task execution
- Some user oversight required
- Tool integration capabilities
- Iterative development
Controlled Autonomy: Codex CLI (Suggest/Auto Edit modes), Standard ChatGPT/Gemini
- User approval for actions
- Step-by-step execution
- Safety-first approach
- Learning-oriented interaction
Specialization Areas
General Purpose: ChatGPT, Gemini Autonomous Execution: Manus AI Code Development: Claude Code CLI, Codex CLI Large-Scale Analysis: Gemini, Gemini CLI Research Integration: Gemini (all versions), Manus AI Safety-Focused: Codex CLI, Claude Code CLI
Best Practices Across All Systems
Universal Principles
- Be specific and clear about requirements and desired outcomes
- Provide comprehensive context relevant to the task
- Specify constraints and limitations upfront
- Define success criteria and quality expectations
- Use examples to illustrate desired formats and styles
System-Specific Optimization
- Match prompting style to interface: Conversational for chat, direct for CLI
- Leverage unique capabilities: Multimodal for Gemini, autonomy for Manus, safety for Codex
- Consider context limitations: Massive for Gemini, automatic for Claude Code
- Utilize integrations: Google ecosystem, development tools, web search
- Respect safety boundaries: Approval modes, sandboxing, version control
Task-Specific Selection
- Research and Analysis: Gemini (large context + search)
- Complete Project Delivery: Manus AI (autonomous execution)
- Code Development: Claude Code CLI or Codex CLI (development integration)
- Content Creation: ChatGPT (conversational refinement)
- Large Codebase Work: Gemini CLI (massive context)
- Safe Experimentation: Codex CLI (approval modes)
Implementation Recommendations
For Development Teams
- Use CLI tools for daily coding tasks and development workflow integration
- Leverage chat interfaces for planning, research, and complex problem-solving
- Apply Manus AI for complete feature delivery and project automation
- Combine systems strategically based on task requirements
For Content Creators
- Start with ChatGPT for iterative content development
- Use Gemini for research-heavy content requiring current information
- Apply Manus AI for complete content campaigns and deliverable packages
- Leverage multimodal capabilities for visual content integration
For Researchers and Analysts
- Use Gemini for comprehensive analysis with large datasets
- Apply search integration for current information and validation
- Leverage Manus AI for complete research reports and presentations
- Combine multiple systems for cross-validation and comprehensive coverage
Conclusion
Each AI system in this collection offers unique strengths and optimal use cases. The key to effective AI utilization lies in:
- Understanding each system's strengths and limitations
- Matching prompting strategies to system capabilities
- Selecting appropriate tools for specific tasks
- Combining systems strategically for comprehensive solutions
- Adapting approaches based on interface type (chat vs CLI)
These guides provide the foundation for maximizing productivity and achieving superior results across the full spectrum of AI-assisted tasks, from quick development fixes to comprehensive project delivery.
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