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

Introduction to LangGraph and Graphical Structures

• The role of graphs in LLM applications: comparing orchestration with simple chains
• Defining nodes, edges, and state within LangGraph
• Getting started: building the first executable graph

State Management and Prompt Chaining Strategies

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

Branching Logic, Control Flow, and Error Resilience

• Designing conditional routing and multi-path workflows
• Configuring retries, timeouts, and fallback mechanisms
• Ensuring idempotency for safe process re-execution

Tool Integration and External Connectivity

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

Workflows Enhanced by Retrieval-Augmentation

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

Quality Assurance: Testing, Debugging, and Evaluation

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

Essentials of Packaging and Deployment

• Setting up environments and managing dependencies
• Exposing graphs via API services
• Managing workflow versions and executing rolling updates

Course Summary and Future Directions