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Course Outline
Introduction to LangGraph and Graph Concepts
- Rationale for using graphs in LLM apps: orchestration versus simple chains
- Core components: nodes, edges, and state within LangGraph
- Hello LangGraph: creating your first runnable graph
State Management and Prompt Chaining
- Designing prompts as distinct graph nodes
- Transferring state between nodes and managing outputs
- Memory patterns: differentiating between short-term and persisted context
Branching, Control Flow, and Error Handling
- Conditional routing and multi-path workflow execution
- Strategies for retries, timeouts, and fallbacks
- Ensuring idempotency and safe re-execution
Tools and External Integrations
- Implementing function/tool calling from graph nodes
- Invoking REST APIs and external services within the graph
- Processing structured outputs
Retrieval-Augmented Workflows
- Fundamentals of document ingestion and chunking
- Leveraging embeddings and vector stores (e.g., ChromaDB)
- Generating grounded responses with citations
Testing, Debugging, and Evaluation
- Writing unit-style tests for nodes and paths
- Implementing tracing and observability features
- Conducting quality checks: assessing factuality, safety, and determinism
Packaging and Deployment Fundamentals
- Setting up environments and managing dependencies
- Exposing graphs via APIs for service
- Managing workflow versioning and rolling updates
Summary and Next Steps
Requirements
- Fundamental knowledge of Python programming
- Experience with REST APIs or command-line interface (CLI) tools
- Understanding of LLM concepts and basic prompt engineering principles
Audience
- Developers and software engineers new to graph-based LLM orchestration
- Prompt engineers and AI practitioners developing multi-step LLM applications
- Data professionals exploring workflow automation using LLMs
14 Hours
