This module focuses on advanced prompting patterns that improve reasoning, task decomposition, and workflow reliability in AI systems. Learners explore techniques such as Step-Back Prompting, Least-to-Most Prompting, Chain-of-Thought, Tree-of-Thought, Prompt Chaining, and Role-Based Prompting through practical demonstrations and guided activities. The module also introduces auto-prompt generation and semantic prompt selection strategies for handling complex and ambiguous real-world workflows.

This module focuses on context engineering and the design of structured prompt workflows for enterprise AI systems. Learners explore different types of context used in LLM-driven applications, including system instructions, retrieved information, memory, and output constraints. Through practical demonstrations and workflow simulations, the module introduces semantic retrieval, structured JSON outputs, and human-in-the-loop refinement techniques for building reliable and context-aware AI workflows.

This module focuses on using retrieval-augmented generation (RAG) and prompt evaluation techniques to improve the reliability of AI-generated responses. Learners explore retrieval strategies, citations, provenance, and validation methods through practical experiments and workflow simulations. The module also introduces A/B testing, quality scoring, and prompt-context evaluation techniques for optimizing enterprise AI workflows.

This module focuses on operational prompting practices related to AI governance, safety, and ethical workflow management. Learners explore prompt templates, adversarial prompting, prompt injection risks, and defense strategies through practical governance scenarios and simulations. The module also introduces secure prompting practices and operational safeguards for building reliable and responsible enterprise AI systems.