**Mastering Prompt Engineering: Reasoning, Reflection and Agentic Design** is a 2-day course focused on advanced prompt-engineering techniques for producing more reliable generative-AI output across business and engineering tasks. Participants explore GenAI fundamentals including tokenization, embeddings, decoding, and model settings that influence output quality. The course covers zero-shot, few-shot, dynamic example selection, chain-of-thought, step-based, meta, and reflective prompting, along with prompt chaining, task decomposition, self-correction loops, and emerging approaches such as Logic-of-Thought and Narrative-of-Thought. It also applies these patterns to agentic AI, tool use, memory integration, ensembling, code prompting, self-consistency, self-criticism, and plan-and-solve. Hands-on labs help learners build a personal prompt library, an agentic prompt set with tool-use scaffolding, and a self-correcting workflow. Designed for business users, analysts, data scientists, and developers with basic GenAI familiarity.