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

Introduction to LangGraph and Graph Concepts

• The rationale for using graphs in LLM applications: advanced orchestration versus simple chains.
• Understanding the roles of nodes, edges, and state within LangGraph.
• Getting started: building and executing your first basic graph.

State Management and Prompt Chaining

• Designing prompts as functional graph nodes.
• Mechanisms for passing state between nodes and managing outputs.
• Memory patterns: distinguishing between short-term and persisted context.

Branching, Control Flow, and Error Handling

• Implementing conditional routing and multi-path workflow structures.
• Managing retries, timeouts, and fallback strategies.
• Ensuring idempotency and facilitating safe re-runs.

Tools and External Integrations

• Executing function and tool calls from within graph nodes.
• Interacting with REST APIs and external services inside the graph structure.
• Processing and utilizing structured outputs.

Retrieval-Augmented Workflows

• Fundamentals of document ingestion and chunking techniques.
• Utilizing embeddings and vector stores, such as ChromaDB.
• Generating grounded answers with proper citations.

Testing, Debugging, and Evaluation

• Writing unit-style tests for individual nodes and workflow paths.
• Implementing tracing and observability best practices.
• Conducting quality assessments for factuality, safety, and determinism.

Packaging and Deployment Fundamentals

• Configuring environments and managing dependencies.
• Serving graph workflows behind API endpoints.
• Managing workflow versioning and implementing rolling updates.

Summary and Next Steps