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Course Outline

Introduction to LangGraph and Graph Concepts

  • The rationale for using graphs in LLM apps: orchestration versus simple chains
  • Understanding nodes, edges, and state within LangGraph
  • Hello LangGraph: creating your first executable graph

State Management and Prompt Chaining

  • Designing prompts as individual 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 workflow execution
  • Strategies for retries, timeouts, and fallbacks
  • Ensuring idempotency and safe re-execution

Tools and External Integrations

  • Implementing function/tool calling from within graph nodes
  • Invoking REST APIs and external services during graph execution
  • Managing structured outputs effectively

Retrieval-Augmented Workflows

  • Basics of document ingestion and chunking
  • Utilizing embeddings and vector stores (e.g., ChromaDB)
  • Achieving grounded answers with proper citations

Testing, Debugging, and Evaluation

  • Conducting unit-style tests for nodes and paths
  • Leveraging tracing and observability tools
  • Performing quality checks: assessing factuality, safety, and determinism

Packaging and Deployment Fundamentals

  • Setting up environments and managing dependencies
  • Deploying graphs behind API endpoints
  • Managing workflow versioning and executing rolling updates

Summary and Next Steps

Requirements

  • Fundamental understanding of Python programming
  • Experience with REST APIs or command-line interface (CLI) tools
  • Familiarity with LLM concepts and the basics of prompt engineering

Audience

  • Developers and software engineers new to graph-based LLM orchestration
  • Prompt engineers and AI beginners constructing multi-step LLM applications
  • Data practitioners exploring workflow automation through LLMs
 14 Hours

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  • Format: Online (live), In-company (at your offices), or Hybrid.
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