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: your first executable graph

State Management and Prompt Chaining

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

Branching, Control Flow, and Error Handling

• Implementing conditional routing and multi-path workflows
• Managing retries, timeouts, and fallback procedures
• Ensuring idempotency and facilitating safe re-executions

Tools and External Integrations

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

Retrieval-Augmented Workflows

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

Testing, Debugging, and Evaluation

• Writing unit-style tests for specific nodes and workflow paths
• Implementing tracing and observability measures
• Conducting quality assessments for factuality, safety, and determinism

Packaging and Deployment Fundamentals

• Configuring environments and managing dependencies
• Exposing graphs via API endpoints
• Managing workflow versioning and implementing rolling updates

Summary and Next Steps