prompt-engineering-patterns

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability. Start with simple prompts, add complexity only when needed: Track these KPIs for your prompts: - 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 - 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 - 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 - JSON mode for reliable parsing - Pydantic schema enforcement - Type-safe response handling - Error handling for malformed outputs - Iterative refinement...