prompt-engineering-patterns
Advanced prompt engineering techniques for optimizing LLM performance, reliability, and structured outputs in production.
- Covers six core capability areas: few-shot learning with dynamic example selection, chain-of-thought reasoning with self-consistency, structured outputs via JSON and Pydantic schemas, iterative prompt optimization, reusable template systems, and role-based system prompt design
- Includes practical patterns for semantic example selection, self-verification workflows, progressive disclosure, error recovery with fallbacks, and integration with RAG systems
- Provides token efficiency strategies, prompt caching for repeated prefixes, and performance monitoring metrics (accuracy, consistency, latency, success rate)
- Emphasizes testing on diverse inputs, versioning prompts as code, and avoiding common pitfalls like over-engineering, context overflow, and ambiguous instructions
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
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