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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.
- Getting started with 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 facilitating safe re-runs.
Tools and External Integrations
- Invoking functions and tools from graph nodes.
- Calling REST APIs and services within the graph structure.
- Managing 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
- 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 environments and managing dependencies.
- Serving graphs via APIs.
- Versioning workflows and executing rolling updates.
Summary and Next Steps
Requirements
- A foundational understanding of Python programming.
- Practical 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 beginners constructing multi-step LLM applications.
- Data practitioners interested in workflow automation using LLMs.
14 Hours
