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