The Generative AI & Prompt Engineering course provides foundational knowledge of Generative AI concepts, prompt engineering techniques, foundation models, and AWS Generative AI services used to build intelligent AI-powered applications. Learners will explore core Generative AI concepts, business use cases, model lifecycles, and prompt engineering strategies for interacting effectively with large language models (LLMs).

The course also covers prompt design techniques, prompt optimization, parameter-efficient fine-tuning, P-tuning, and A/B testing approaches used to improve AI responses and model performance. In addition, learners will explore Amazon Q services and Amazon Bedrock, including foundation model selection, Guardrails, Knowledge Bases, RAG architectures, Agents, integrations, and Generative AI application development on AWS. This course is structured into three modules with approximately 6–8 hours of video content and quizzes to reinforce learning. Course Modules: Module 1: Generative AI Foundations Module 2: Prompt Engineering Module 3: Amazon Q & Bedrock By the end of this course, learners will be able to: Understand core Generative AI concepts, foundation models, and AI use cases Understand prompt engineering principles and effective prompt design techniques Explore fine-tuning, prompt learning, and model optimization approaches Understand Retrieval-Augmented Generation (RAG) architectures and vector embeddings Explore Amazon Q services for business and developer productivity Understand Amazon Bedrock, foundation models, Guardrails, Agents, and AI integrations Identify appropriate Generative AI services and architectures for different business and application requirements This course is ideal for learners preparing for Generative AI application development, AI-powered cloud solutions, prompt engineering roles, and foundational AWS AI certification learning.