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Course Outline
Introduction to LangGraph and Graph Concepts
- Rationale for using graphs in LLM apps: orchestration versus simple chains
- Nodes, edges, and state in LangGraph
- LangGraph basics: creating the first runnable graph
State Management and Prompt Chaining
- Designing prompts as graph nodes
- Passing 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 workflows
- Strategies for retries, timeouts, and fallbacks
- Ensuring idempotency and enabling safe re-executions
Tools and External Integrations
- Implementing function/tool calling within graph nodes
- Invoking REST APIs and services within the graph
- Handling structured outputs
Retrieval-Augmented Workflows
- Basics of document ingestion and chunking
- Embeddings and vector stores (e.g., ChromaDB)
- Generating grounded answers with citations
Testing, Debugging, and Evaluation
- Writing unit-style tests for nodes and paths
- Utilizing tracing and observability tools
- Conducting quality checks: factuality, safety, and determinism
Packaging and Deployment Fundamentals
- Setting up environments and managing dependencies
- Deploying graphs behind APIs
- Versioning workflows and executing rolling updates
Summary and Next Steps
Requirements
- Familiarity with basic Python programming
- Experience with REST APIs or CLI tools
- Understanding of LLM concepts and fundamental prompt engineering techniques
Audience
- Developers and software engineers who are new to graph-based LLM orchestration
- Prompt engineers and AI newcomers developing multi-step LLM applications
- Data professionals exploring workflow automation with LLMs
14 Hours
