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Course Outline
Introduction to LangGraph and Graph Concepts
- The rationale for using graphs in LLM apps: orchestration compared to simple chains
- Understanding nodes, edges, and state in LangGraph
- Hello LangGraph: creating the first runnable graph
State Management and Prompt Chaining
- Designing prompts as graph nodes
- Transmitting state between nodes and managing outputs
- Memory patterns: distinguishing between short-term and persisted context
Branching, Control Flow, and Error Handling
- Conditional routing and multi-path workflow structures
- Implementing retries, timeouts, and fallback strategies
- Ensuring idempotency and safe re-execution
Tools and External Integrations
- Function and tool calling from graph nodes
- Invoking REST APIs and services within the graph
- Handling structured outputs
Retrieval-Augmented Workflows
- Basics of document ingestion and chunking
- Understanding embeddings and vector stores (e.g., ChromaDB)
- Generating grounded answers with citations
Testing, Debugging, and Evaluation
- Conducting unit-style tests for nodes and paths
- Utilizing tracing and observability tools
- Performing quality checks for factuality, safety, and determinism
Packaging and Deployment Fundamentals
- Environment setup and dependency management
- Exposing graphs via APIs
- Managing workflow versioning and rolling updates
Summary and Next Steps
Requirements
- A foundational understanding of Python programming
- Experience with REST APIs or CLI tools
- Familiarity with LLM concepts and the fundamentals of prompt engineering
Audience
- Developers and software engineers new to graph-based LLM orchestration
- Prompt engineers and AI beginners developing multi-step LLM applications
- Data practitioners exploring workflow automation using LLMs
14 Hours
