prompt▌

Advanced prompt engineering techniques for optimizing LLM performance, reliability, and structured outputs in production. \n \n 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 \n Includes practical patterns for semantic example selection, self-verification workflows, pr

Expert guidance for designing, testing, and optimizing prompts that reliably guide LLM behavior. \n \n Covers six core capabilities: prompt design and optimization, system prompt architecture, context window management, output format specification, few-shot example design, and prompt testing and evaluation \n Provides structured patterns for system prompts, few-shot examples, and chain-of-thought reasoning with explicit anti-patterns and sharp edges to avoid \n Emphasizes systematic evaluation a