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Course Outline

Overview of LangGraph and Graph Fundamentals

  • Rationale for employing graph structures in large language model applications: orchestrating complex processes versus linear chains
  • Core components: nodes, edges, and state management within LangGraph
  • Initial implementation: creating the first executable graph

State Management and Sequential Prompting

  • Structuring prompts as distinct graph nodes
  • Transferring state between nodes and processing outputs
  • Memory architectures: distinguishing short-term context from persistent storage

Control Flow, Branching Logic, and Error Resilience

  • Conditional routing and execution of parallel or divergent workflows
  • Strategies for retries, timeout handling, and operational fallbacks
  • Ensuring idempotency and reliability during re-execution

Integration with External Tools and Services

  • Utilizing function or tool calls within graph nodes
  • Integrating REST APIs and external services into graph workflows
  • Processing and managing structured data outputs

Retrieval-Augmented Generation Workflows

  • Fundamentals of document ingestion and text chunking
  • Implementation of embeddings and vector databases, such as ChromaDB
  • Generating grounded responses with source citations

Testing, Debugging, and Quality Assurance

  • Conducting unit-style testing for individual nodes and execution paths
  • Monitoring system behavior through tracing and observability tools
  • Evaluating output quality: verifying factuality, safety, and deterministic behavior

Deployment and Operational Readiness

  • Configuring runtime environments and managing dependencies for government applications
  • Deploying graph services via application programming interfaces (APIs)
  • Managing workflow version control and executing rolling updates

Conclusion and Strategic Next Steps

Requirements

  • Fundamental proficiency in Python programming languages
  • Practical application of RESTful APIs or command-line interface utilities
  • Conceptual knowledge of large language models and core principles of prompt engineering

Target Audience

  • Software engineers and developers initiating work in graph-based orchestration of large language models
  • Prompt specialists and artificial intelligence practitioners developing complex, multi-stage AI applications
  • Data professionals investigating workflow automation capabilities utilizing large language models for government initiatives
 14 Hours

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