prompt-engineering

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

(Select at least one profile to enable specific modules)

• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

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

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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

1. Review requirements and constraints
2. Set up development environment
3. Implement core functionality following patterns
4. Write tests for critical paths
5. Run tests and fix issues
6. Document any deviations or decisions