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