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

Introduction to LangGraph and Graph Principles

• The role of graphs in LLM applications: orchestration versus simple chaining
• Understanding nodes, edges, and state within LangGraph
• Hello LangGraph: Building your first executable graph

State Management and Prompt Chaining

• Structuring prompts as graph nodes
• Transferring state between nodes and managing outputs
• Memory strategies: distinguishing between short-term and persistent context

Branching, Control Flow, and Error Management

• Implementing conditional routing and multi-path workflows
• Managing retries, timeouts, and fallback mechanisms
• Ensuring idempotency and secure re-executions

Tools and External Integrations

• Executing function/tool calls from graph nodes
• Accessing REST APIs and services within the graph structure
• Handling structured outputs effectively

Retrieval-Augmented Workflows

• Fundamentals of document ingestion and chunking
• Utilising embeddings and vector databases (e.g., ChromaDB)
• Generating grounded responses with citations

Testing, Debugging, and Evaluation

• Writing unit-style tests for nodes and pathways
• Implementing tracing and observability features
• Quality assurance: verifying factuality, safety, and consistency

Basics of Packaging and Deployment

• Setting up environments and managing dependencies
• Exposing graphs via APIs
• Versioning workflows and executing rolling updates

Recap and Future Steps