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Course Outline
Introduction to LangGraph and Graph Concepts
- Understanding why graphs are used for LLM apps: orchestration versus simple chains.
- Exploring nodes, edges, and state within LangGraph.
- Hello LangGraph: 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
- Implementing conditional routing and multi-path workflows.
- Handling retries, timeouts, and fallback strategies.
- Ensuring idempotency and safe re-runs.
Tools and External Integrations
- Invoking functions and tools from graph nodes.
- Calling REST APIs and services directly within the graph.
- Working with structured outputs.
Retrieval-Augmented Workflows
- Basics of document ingestion and chunking.
- Using embeddings and vector stores (e.g., ChromaDB).
- Providing grounded answers with citations.
Testing, Debugging, and Evaluation
- Conducting unit-style tests for nodes and paths.
- Implementing tracing and observability.
- Performing quality checks for factuality, safety, and determinism.
Packaging and Deployment Fundamentals
- Setting up the environment and managing dependencies.
- Serving graphs behind APIs.
- Versioning workflows and implementing rolling updates.
Summary and Next Steps
Requirements
- A foundational understanding of Python programming.
- Experience working 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 newcomers developing multi-step LLM applications.
- Data practitioners exploring workflow automation using LLMs.
14 Hours
