Overview

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This course explores the full spectrum of Prompt Engineering, equipping AI practitioners, developers, and knowledge workers with the skills to design, optimize, and govern high-performance prompts for large language models. You'll begin by mastering the foundations of LLM interactions including effective prompting techniques, few-shot patterns, and self-consistency prompting using tools like Google AI Studio and simulated chat environments. You'll then advance through chain-of-thought reasoning, tree-of-thought exploration, role-based prompting, and prompt chaining before diving into context engineering, structured output design, and human-in-the-loop workflows. Finally, you'll tackle external knowledge integration through RAG pipelines, prompt A/B testing and quality evaluation, and operational governance including adversarial prompting defenses and ethical prompt practices. By the end of this program, you will be able to: - Apply foundational and advanced prompting techniques to solve complex, real-world AI tasks. - Design multi-step prompt workflows using chaining, auto-prompting, and context engineering. - Build and evaluate RAG pipelines with citation-grounded, provenance-aware responses. - Measure and optimize prompt quality through A/B testing and iterative refinement. - Govern prompt systems responsibly by defending against injection attacks and applying ethical principles. This program is designed for AI enthusiasts, developers, and business professionals who want to move beyond basic AI usage and build reliable, scalable, and responsible prompt-driven systems. Familiarity with AI chat tools will help you get the most from this experience. Join us to unlock the full potential of Prompt Engineering and gain the technical fluency to shape, steer, and safeguard AI outputs across any domain.

Syllabus

Foundations of LLM Interactions

This module focuses on the foundational concepts of prompt engineering and effective interaction techniques for large language models. Learners explore how prompt structure, instructions, examples, and context influence AI-generated outputs through practical demonstrations and guided experiments. The module also introduces prompt refinement, few-shot prompting, self-consistency techniques, and prompt debugging workflows to build reliable and high-quality AI interactions.

Advanced Prompting Patterns

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.

Designing Complex Prompts and Context for Real 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.

Using External Knowledge and Evaluating Prompt Quality

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.

Operational Prompting: Governance, Safety, and Ethics

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.