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

Getting Started with LangGraph and Graph Architecture

• The rationale behind using graphs for LLM applications: orchestration advantages over simple linear chains
• Understanding the core components: nodes, edges, and state within LangGraph
• Practical introduction: building and executing your first functional graph

State Management and Prompt Sequencing

• Architecting prompts as discrete graph nodes
• Managing data flow: passing state between nodes and processing outputs
• Implementing memory strategies: distinguishing between short-term session data and persisted context

Conditional Logic, Control Flow, and Robustness

• Implementing conditional routing and multi-path workflow logic
• Managing resilience: configuring retries, timeouts, and fallback mechanisms
• Ensuring system integrity through idempotency and safe re-execution practices

Tool Integration and External Service Connectivity

• Executing function and tool calls directly from graph nodes
• Interacting with REST APIs and external services within the graph structure
• Processing and utilizing structured output formats

Retrieval-Augmented Generation (RAG) Workflows

• Fundamentals of document ingestion and text chunking
• Leveraging embeddings and vector databases, such as ChromaDB
• Generating grounded, citation-backed responses for improved accuracy

Quality Assurance: Testing, Debugging, and Evaluation

• Writing unit-style tests for individual nodes and workflow paths
• Implementing tracing mechanisms and observability features
• Conducting quality assessments focused on factuality, safety compliance, and deterministic behavior

Deployment Strategies and Packaging Essentials

• Configuring development environments and managing dependencies
• Exposing graph capabilities via API endpoints
• Managing workflow versioning and implementing rolling update strategies

Course Recap and Future Development Paths