Prompt Engineering Guide
Under ConstructionThis documentation is currently being developed and will be available soon. This placeholder outlines the planned content.
Overview
Prompt engineering is the practice of designing effective inputs to AI agents to achieve desired outputs. This guide provides best practices, techniques, and examples for crafting prompts that work effectively with the MOOD MNKY agent system.Planned Content
Prompt Engineering Fundamentals
- Basic prompt structure
- Context and instruction clarity
- Specificity vs. flexibility
- Token efficiency considerations
- Agent-specific optimization
Agent-Specific Techniques
MOOD MNKY
Techniques for customer experience and personalization
- Emotional context inclusion
- Preference elicitation
- Sensory detail prompting
CODE MNKY
Techniques for technical support and development
- Problem specification formats
- System context inclusion
- Technical detail structuring
SAGE MNKY
Techniques for learning and content creation
- Educational goal specification
- Knowledge assessment prompts
- Content creation frameworks
Use Case Examples
- Customer support scenarios
- Product recommendation workflows
- Educational content creation
- Technical troubleshooting
- Community facilitation
Common Patterns
- Chain-of-thought prompting
- Few-shot learning examples
- System message optimization
- Conversation management
- Response format specification
Troubleshooting Techniques
- Identifying prompt issues
- Iterative refinement processes
- Testing and evaluation methods
- Handling unexpected responses
- Prompt versioning and management
Interactive Examples
When completed, this guide will include interactive examples that demonstrate:- Before/after prompt comparisons
- Effect of different prompt structures
- Agent-specific optimization techniques
- Common pitfalls and solutions
- A/B testing of prompt variations
The full prompt engineering guide will include downloadable prompt templates, case studies of successful implementations, and a collaborative prompt library where the community can share effective patterns.