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Course Outline
Introduction to LangGraph and Graph Concepts
- Rationale for using graphs in LLM applications: orchestration versus simple chains.
- Understanding nodes, edges, and state in LangGraph.
- Hello LangGraph: creating your 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 workflows.
- Strategies for retries, timeouts, and fallbacks.
- 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.
- Utilizing embeddings and vector stores (e.g., ChromaDB).
- Providing grounded answers with citations.
Testing, Debugging, and Evaluation
- Unit-style tests for nodes and paths.
- Tracing and observability.
- Quality checks: assessing factuality, safety, and determinism.
Packaging and Deployment Fundamentals
- Environment setup and dependency management.
- Exposing graphs via APIs.
- Versioning workflows and managing rolling updates.
Summary and Next Steps
Requirements
- Basic proficiency in Python programming.
- Experience with REST APIs or CLI tools.
- Familiarity with Large Language Model (LLM) concepts and the fundamentals of prompt engineering.
Target Audience
- Developers and software engineers new to graph-based LLM orchestration.
- Prompt engineers and AI enthusiasts building multi-step LLM applications.
- Data professionals exploring workflow automation through LLMs.
14 Hours
