Advanced Prompting and Context Engineering

About this course

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.