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

Overview of LangGraph and Graph Architecture

• Advantages of graph-based architectures for large language model (LLM) applications: orchestration compared to linear chains
• Core components: nodes, edges, and state management within LangGraph
• Initial implementation: constructing a functional graph

State Management and Prompt Sequencing

• Structuring prompts as distinct graph nodes
• Transmitting state between nodes and processing outputs
• Memory architectures: distinguishing between transient and persistent context

Conditional Logic, Control Flow, and Resilience

• Dynamic routing and multi-path execution workflows
• Strategies for retries, timeouts, and fallback mechanisms
• Ensuring idempotency and secure re-execution

Integration with External Tools and Services

• Executing functions and tool calls from graph nodes
• Interfacing with REST APIs and external services within the graph
• Managing structured data outputs

Retrieval-Augmented Generation Workflows

• Principles of document ingestion and chunking
• Utilizing embeddings and vector databases (e.g., ChromaDB) for data storage
• Implementing grounded responses with verifiable citations

Testing, Debugging, and Assessment

• Conducting unit-level tests for nodes and execution paths
• Monitoring and observability through tracing
• Evaluating quality metrics: factual accuracy, safety compliance, and deterministic behavior

Packaging and Deployment Essentials

• Configuring environments and managing dependencies
• Deploying graphs via application programming interfaces (APIs)
• Implementing version control for workflows and managing rolling updates

Summary and Strategic Direction