AWS Generative AI Development with Bedrock and LangChain

Generative artificial intelligence is transforming how we build software, but integrating these models into secure, scalable cloud environments can feel overwhelming. This course simplifies the process, guiding you through the essentials of cloud-based AI development. You will transition from understanding basic AI concepts to designing and implementing your own intelligent applications on AWS. Through clear, written explanations and practical code walkthroughs, you will gain the skills needed to orchestrate foundation models, build autonomous agents, and connect them to external data sources. What you'll learn: - Understand the foundational principles of generative AI, large language models, and cloud-based AI architecture. - Configure foundation models using AWS Bedrock to generate text, summarize documents, and process natural language. - Build autonomous AI agents that can plan, reason, and execute tasks using LangChain frameworks. - Implement Retrieval-Augmented Generation (RAG) patterns to connect your models to secure vector databases and custom knowledge bases. - Apply prompt engineering best practices to optimize model outputs for specific business use cases. - Leverage AWS Q Developer to accelerate your coding workflow and debug application logic efficiently. The course begins with core definitions and cloud AI fundamentals before moving step-by-step into API integration, agent creation, and RAG implementation. You will read through conceptual explanations and analyze structured code examples to build confidence at every stage. This course is designed for software developers, cloud enthusiasts, and technology professionals who are new to generative AI on AWS. No prior experience with machine learning or AI modeling is required, though a basic familiarity with Python and general cloud concepts will help you get the most out of the material. Start reading today to begin building intelligent cloud-native AI applications.