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

Introduction to LangGraph and Graph Principles

• The rationale for using graphs in LLM apps: orchestration versus simple linear chains
• Understanding nodes, edges, and state within the LangGraph framework
• Getting started with LangGraph: creating your first executable graph

State Management and Prompt Chaining

• Structuring prompts as individual graph nodes
• Managing state transfer between nodes and processing outputs
• Implementing memory patterns: distinguishing between short-term and persisted context

Branching, Control Flow, and Error Management

• Implementing conditional routing and managing multi-path workflows
• Configuring retries, timeouts, and establishing fallback strategies
• Ensuring idempotency and facilitating safe re-executions

Tools and External Integrations

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

Retrieval-Augmented Workflows

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

Testing, Debugging, and Evaluation

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

Packaging and Deployment Fundamentals

• Setting up environments and managing dependencies
• Serving graph applications via APIs
• Managing workflow versions and executing rolling updates

Summary and Future Directions