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

Introduction to LangGraph and Graph Concepts

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

State Management and Prompt Chaining

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

Branching, Control Flow, and Error Handling

• 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 within the graph structure
• Handling structured outputs effectively

Retrieval-Augmented Workflows

• Fundamentals of document ingestion and chunking
• Utilizing embeddings and vector stores (such as ChromaDB)
• Providing grounded answers with proper citations

Testing, Debugging, and Evaluation

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

Packaging and Deployment Fundamentals

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

Summary and Next Steps