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Course Outline
Introduction to LangGraph and Graph Concepts
- Why use graphs for LLM applications: orchestration versus simple chains
- Nodes, edges, and state within LangGraph
- Hello LangGraph: creating your first runnable graph
State Management and Prompt Chaining
- Designing prompts as graph nodes
- Passing state between nodes and handling outputs
- Memory patterns: distinguishing between short-term and persisted context
Branching, Control Flow, and Error Handling
- Conditional routing and multi-path workflows
- Strategies for retries, timeouts, and fallbacks
- Ensuring idempotency and safe re-runs
Tools and External Integrations
- Function and tool calling from within graph nodes
- Invoking REST APIs and services within the graph
- Working with structured outputs
Retrieval-Augmented Workflows
- Basics of document ingestion and chunking
- Embeddings and vector stores (e.g., ChromaDB)
- Grounded answering with citations
Testing, Debugging, and Evaluation
- Unit-style tests for nodes and paths
- Tracing and observability
- Quality checks: ensuring factuality, safety, and determinism
Packaging and Deployment Fundamentals
- Environment setup and dependency management
- Serving graphs via APIs
- Versioning workflows and managing rolling updates
Summary and Next Steps
Requirements
- A fundamental understanding of Python programming
- Experience working with REST APIs or CLI tools
- Familiarity with LLM concepts and the fundamentals of prompt engineering
Audience
- Developers and software engineers who are new to graph-based LLM orchestration
- Prompt engineers and AI beginners developing multi-step LLM applications
- Data practitioners investigating workflow automation using LLMs
14 Hours
