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

Introduction to LangGraph and Graph Fundamentals

• The role of graphs in LLM applications: orchestration compared to simple chains
• Understanding nodes, edges, and state within LangGraph
• Getting started with LangGraph: building your first executable graph

State Management and Prompt Chaining

• Structuring prompts as distinct graph nodes
• Managing state transfer between nodes and processing outputs
• Memory strategies: distinguishing between short-term and persisted context

Branching, Control Flow, and Error Management

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

Tools and External Integrations

• Executing function and tool calls from graph nodes
• Interacting with REST APIs and services within the graph structure
• Processing and utilizing structured data outputs

Retrieval-Augmented Workflows

• Basics of document ingestion and data chunking
• Utilizing embeddings and vector stores (e.g., ChromaDB)
• Generating grounded answers with proper citations

Testing, Debugging, and Evaluation

• Writing unit-style tests for individual nodes and workflow paths
• Implementing tracing and observability features
• Conducting quality assessments for factuality, safety, and deterministic behavior

Packaging and Deployment Essentials

• Configuring development environments and managing dependencies
• Exposing graphs as backend services via APIs
• Versioning workflows and managing rolling updates

Summary and Future Directions