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

Foundations of LangGraph and Graph Theory

• The rationale for using graphs in LLM applications: Orchestration versus linear chains
• Understanding nodes, edges, and state within LangGraph
• Getting started: Executing the first basic graph

State Management and Prompt Chaining

• Structuring prompts as individual graph nodes
• Transferring state across nodes and managing output streams
• Memory architectures: Differentiating between ephemeral and persistent context

Branching, Control Logic, and Exception Management

• Implementing conditional routing and diverse workflow paths
• Configuring retry mechanisms, timeouts, and fallback procedures
• Ensuring idempotency and safe execution reruns

Tools and External System Integrations

• Invoking functions and tools from graph nodes
• Interacting with REST APIs and external services within the graph structure
• Processing structured data outputs

Retrieval-Augmented Generation (RAG) Workflows

• Basics of document ingestion and text segmentation
• Utilizing embeddings and vector databases (e.g., ChromaDB)
• Generating grounded responses with proper source citations

Testing, Debugging, and Performance Evaluation

• Writing unit-level tests for individual nodes and workflow paths
• Implementing tracing and observability tools
• Quality assurance: Verifying factuality, safety, and deterministic behavior

Packaging and Deployment Essentials

• Setting up development environments and managing dependencies
• Exposing graph workflows via API endpoints
• Managing workflow versions and implementing smooth rolling updates

Conclusions and Future Directions