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

Introduction to LangGraph and Graph Concepts

• Why graphs are preferred for LLM applications: orchestration versus simple chains
• Understanding nodes, edges, and state within LangGraph
• Hello LangGraph: creating the first executable graph

State Management and Prompt Chaining

• Designing prompts as individual graph nodes
• Transferring state between nodes and managing outputs
• Memory patterns: distinguishing between short-term and persisted context

Branching, Control Flow, and Error Handling

• Conditional routing and multi-path workflows
• Implementing retries, timeouts, and fallback strategies
• Ensuring idempotency and safe re-execution

Tools and External Integrations

• Function and tool invocation from graph nodes
• Interacting with REST APIs and services within the graph structure
• Handling structured outputs

Retrieval-Augmented Workflows

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

Testing, Debugging, and Evaluation

• Unit-style testing for nodes and paths
• Tracing and observability techniques
• Quality checks for factuality, safety, and determinism

Packaging and Deployment Fundamentals

• Environment setup and dependency management
• Serving graphs via APIs
• Versioning workflows and performing rolling updates

Summary and Next Steps