Module 3: Building Applications with Foundation Models

Modules 1 and 2 covered how foundation models work and how to use them responsibly. Module 3 is where you build. You will assemble real applications: move from one-off prompts to stateful conversations, ground models in your own data, give them tools so they can act, and extend everything to images and other modalities. The connective tissue is LangChain, a framework for composing LLM-powered applications, running on Amazon Bedrock models.

No.	Chapter	What you will build toward
1	Chapter 1: LangChain Modules	The LangChain framework: prompt templates, output parsers, chains (LCEL), and memory.
2	Chapter 2: Developing Conversational Applications	Chatbots: from Q&A to conversation, chat models, caching, and memory strategies for long chats.
3	Chapter 3: Retrieval-Augmented Generation	Grounding, RAG architecture, vector databases, chunking, rerankers, and multimodal RAG.
4	Chapter 4: Agents	Agents: tools, the ReAct pattern, LangChain agents, and agentic workflows.
5	Chapter 5: Multimodal Applications	Multimodal applications: personalization, video analysis, customer service, and multimodal agents.

The chapters build on one another: a chatbot is a chain with memory; RAG adds retrieval to that chain; an agent adds tools and reasoning on top; and multimodal applications extend the whole stack to images and video. The labs in Module 3 Labs: Building applications implement each step on Amazon Bedrock.