01Prompt Engineering - Module OverviewEngineering system prompts, few-shot examples, and robust prompt pipelines for production LLMs.02Prompt Design FundamentalsMaster the first principles of prompt engineering - clarity, specificity, task framing, structural markers, and the systematic principles behind effective LLM instructions.03System Prompts and PersonasDesign production-grade system prompts and AI personas - the 6-component anatomy, dynamic context injection, behavioral constraints, tone configuration, and persona stability testing.04Few-Shot Learning and Chain-of-Thought PromptingMaster few-shot example selection, chain-of-thought reasoning, self-consistency decoding, and when to use each technique for reliable LLM outputs.05Prompt Templates and CompositionBuild maintainable, production-grade prompt systems with Jinja2 templates, variable injection, modular composition, and reusable prompt libraries.06Prompt Versioning and ManagementTreat prompts as code - semantic versioning, A/B testing, rollback strategies, and prompt registries for production LLM systems.07Prompt Injection DefenseUnderstand prompt injection attack taxonomy, detection strategies, defense layers, and sanitization techniques for production LLM systems.08Prompt Debugging MethodologySystematic methodology for diagnosing and fixing prompt failures - isolation, ablation, root cause analysis, and building a regression test suite.09Advanced Prompting TechniquesMaster self-refinement, Tree of Thought, ReAct, meta-prompting, and other advanced techniques for reliable, sophisticated LLM behavior in production.
