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

Introduction to LangGraph and Graphical Concepts

• The rationale for using graphs in LLM apps: orchestration versus simple chains
• Understanding nodes, edges, and state within LangGraph
• Getting started with LangGraph: building the first executable graph

State Management and Prompt Sequences

• Structuring prompts as individual graph nodes
• Transferring state across nodes and managing output
• Memory strategies: comparing short-term vs. persistent context

Branching, Control Logic, and Error Management

• Implementing conditional routing and multi-path processes
• Managing retries, timeouts, and fallback mechanisms
• Ensuring idempotency and safe re-execution

Tools and External Connections

• Invoking functions and tools from graph nodes
• Interacting with REST APIs and services inside the graph
• Handling structured data outputs

Retrieval-Augmented Processes

• Basics of document intake and segmentation
• Using embeddings and vector databases (e.g., ChromaDB)
• Generating grounded responses with source citations

Testing, Troubleshooting, and Assessment

• Creating unit-style tests for specific nodes and pathways
• Implementing tracing and observability measures
• Conducting quality assurance: factuality, safety, and determinism

Deployment and Packaging Essentials

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

Conclusion and Future Directions