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
• Core elements: nodes, edges, and state in LangGraph
• Getting started with LangGraph: building a first executable graph

State Management and Prompt Chaining

• Structuring prompts as 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 workflow design
• Implementing retries, timeouts, and fallback mechanisms
• Ensuring idempotency and safe re-execution

Tools and External Integrations

• Invoking functions and tools from within graph nodes
• Interacting with REST APIs and services inside the graph structure
• Handling structured data outputs

Retrieval-Augmented Workflows

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

Testing, Debugging, and Evaluation

• Developing unit-style tests for nodes and paths
• Implementing tracing and observability measures
• Conducting quality checks for factuality, safety, and determinism

Packaging and Deployment Fundamentals

• Configuring environments and managing dependencies
• Hosting graphs behind API interfaces
• Versioning workflows and managing rolling updates

Summary and Next Steps