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 chaining
• Understanding nodes, edges, and state within LangGraph
• Getting started: building your first executable graph

State Management and Prompt Chaining

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

Branching, Control Flow, and Error Management

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

Tools and External Integrations

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

Retrieval-Augmented Workflows

• Basics of document ingestion and text chunking
• Utilizing embeddings and vector databases (such as ChromaDB)
• Generating grounded responses with accurate citations

Testing, Debugging, and Evaluation

• Writing unit-style tests for specific nodes and execution paths
• Implementing tracing and observability features
• Performing quality assessments for factuality, safety, and consistency

Packaging and Deployment Basics

• Configuring environments and managing dependencies
• Serving graph applications via API endpoints
• Managing workflow versioning and implementing rolling updates

Conclusion and Future Directions