prompt-engineering-patterns

# Prompt Engineering Patterns

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.

## When to Use This Skill

- Designing complex prompts for production LLM applications
- Optimizing prompt performance and consistency
- Implementing structured reasoning patterns (chain-of-thought, tree-of-thought)
- Building few-shot learning systems with dynamic example selection
- Creating reusable prompt templates with variable interpolation
- Debugging and refining prompts that produce inconsistent outputs
- Implementing system prompts for specialized AI assistants
- Using structured outputs (JSON mode) for reliable parsing

## Core Capabilities

### 1. Few-Shot Learning

- Example selection strategies (semantic similarity, diversity sampling)
- Balancing example count with context window constraints
- Constructing effective demonstrations with input-output pairs
- Dynamic example retrieval from knowledge bases
- Handling edge cases through strategic example selection

### 2. Chain-of-Thought Prompting

- Step-by-step reasoning elicitation
- Zero-shot CoT with "Let's think step by step"
- Few-shot CoT with reasoning traces
- Self-consistency techniques (sampling multiple reasoning paths)
- Verification and validation steps

### 3. Structured Outputs

- JSON mode for reliable parsing
- Pydantic schema enforcement
- Type-safe response handling
- Error handling for malformed outputs

### 4. Prompt Optimization

- Iterative refinement workflows
- A/B testing prompt variations
- Measuring prompt performance metrics (accuracy, consistency, latency)
- Reducing token usage while maintaining quality
- Handling edge cases and failure modes

### 5. Template Systems

- Variable interpolation and formatting
- Conditional prompt sections
- Multi-turn conversation templates
- Role-based prompt composition
- Modular prompt components

### 6. System Prompt Design

- Setting model behavior and constraints
- Defining output formats and stru