| id | SKL-prompt-PROMPTENGINEERING |
| name | Prompt Engineering |
| description | Comprehensive guide for LLM prompt engineering techniques and best practices. Prompt engineering is the art and science of crafting effective prompts to elicit desired outputs from language models. |
| version | 1.0.0 |
| status | active |
| owner | @cerebra-team |
| last_updated | 2026-02-22 |
| category | Backend |
| tags | ["api","backend","server","database"] |
| stack | ["Python","Node.js","REST API","GraphQL"] |
| difficulty | Intermediate |
Prompt Engineering
Skill Profile
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Overview
Comprehensive guide for LLM prompt engineering techniques and best practices. Prompt engineering is the art and science of crafting effective prompts to elicit desired outputs from language models.
Why This Matters
Prompt engineering is critical for:
- Performance: Well-crafted prompts reduce inference time and cost
- Accuracy: Clear instructions improve output quality
- Consistency: Standardized prompts ensure predictable behavior
- Cost Optimization: Efficient prompts reduce token usage
- Maintainability: Reusable templates are easier to maintain
- Model Flexibility: Good prompts work across different models
Core Concepts & Rules
1. Core Principles
- Follow established patterns and conventions
- Maintain consistency across codebase
- Document decisions and trade-offs
2. Implementation Guidelines
- Start with the simplest viable solution
- Iterate based on feedback and requirements
- Test thoroughly before deployment
Inputs / Outputs / Contracts
Skill Composition
- Depends on: llm
- Compatible with: llm, rag
- Conflicts with: None
- Related Skills: llm, rag, agents
Quick Start / Implementation Example
- Review requirements and constraints
- Set up development environment
- Implement core functionality following patterns
- Write tests for critical paths
- Run tests and fix issues
- Document any deviations or decisions
