LangGraph Foundations: Graph-Based LLM Prompting and Chaining Training Course

Introduction to LangGraph and Graph Principles

• The role of graphs in LLM apps: orchestration versus simple chains
• Understanding nodes, edges, and state within LangGraph
• First steps in LangGraph: building your initial runnable graph

State Management and Prompt Chaining

• Structuring prompts as individual graph nodes
• Transferring state across nodes and processing outputs
• Memory strategies: distinguishing short-term from persistent context

Branching, Control Flow, and Error Management

• Implementing conditional routing and multi-path workflows
• Configuring retries, timeouts, and fallback mechanisms
• Ensuring idempotency and safe re-execution

Tools and External Integrations

• Invoking functions and tools from graph nodes
• Interacting with REST APIs and services inside the graph
• Handling structured outputs effectively

Retrieval-Augmented Workflows

• Basics of document ingestion and chunking
• Utilizing embeddings and vector stores (such as ChromaDB)
• Generating grounded answers with citations

Testing, Debugging, and Evaluation

• Writing unit-style tests for nodes and paths
• Implementing tracing and observability
• Conducting quality checks for factuality, safety, and determinism

Packaging and Deployment Basics

• Setting up environments and managing dependencies
• Exposing graphs via APIs
• Versioning workflows and managing rolling updates

Conclusion and Future Steps