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Course Outline
Introduction to LangGraph and Graph Concepts
- The rationale for using graphs in LLM applications: orchestration versus simple chains.
- Understanding nodes, edges, and state within LangGraph.
- Hello LangGraph: creating your first runnable graph.
State Management and Prompt Chaining
- Designing prompts as nodes within a graph.
- 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 enabling safe re-runs.
Tools and External Integrations
- Executing function/tool calls directly from graph nodes.
- Invoking REST APIs and external services within the graph.
- Managing structured outputs effectively.
Retrieval-Augmented Workflows
- Basics of document ingestion and chunking.
- Utilizing embeddings and vector stores (e.g., ChromaDB).
- Generating grounded answers with citations.
Testing, Debugging, and Evaluation
- Conducting unit-style tests for nodes and paths.
- Implementing tracing and observability mechanisms.
- Performing quality checks for factuality, safety, and determinism.
Packaging and Deployment Fundamentals
- Setting up environments and managing dependencies.
- Serving graphs via APIs.
- Versioning workflows and executing rolling updates.
Summary and Next Steps
Requirements
- Fundamental understanding of Python programming.
- Practical experience with REST APIs or command-line interface (CLI) tools.
- Familiarity with core LLM concepts and the fundamentals of prompt engineering.
Target Audience
- Developers and software engineers who are new to graph-based LLM orchestration.
- Prompt engineers and AI beginners focused on building multi-step LLM applications.
- Data practitioners exploring workflow automation through the use of LLMs.
14 Hours
