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 Duration 14 hours

Course Outline

Overview of LangGraph Architecture and Graph-Based Design Principles

  • Rationale for graph-based orchestration in LLM applications compared to linear chains
  • Defining nodes, edges, and state management within LangGraph
  • Initial implementation: Developing a basic functional graph

State Persistence and Sequential Prompt Engineering

  • Structuring prompts as individual graph components
  • Managing data flow and output processing between graph nodes
  • Implementing memory architectures for transient and persistent context

Logical Branching, Execution Control, and Resilience

  • Configuring conditional logic and complex multi-path workflows
  • Establishing retry mechanisms, timeout constraints, and fallback protocols
  • Ensuring idempotency and safe execution of re-runnable processes

External Tool Integration and API Interaction

  • Executing function and tool calls from within graph nodes
  • Interfacing with RESTful services and external applications
  • Processing and validating structured data outputs

Retrieval-Augmented Generation (RAG) Workflows

  • Foundations of document ingestion and segmentation strategies
  • Utilizing embeddings and vector databases (e.g., ChromaDB)
  • Generating cited responses to ensure factual grounding

Quality Assurance, Debugging, and Performance Evaluation

  • Developing unit tests for individual nodes and execution paths
  • Implementing tracing mechanisms and observability standards
  • Conducting audits for factuality, safety compliance, and deterministic behavior

Operational Packaging and Deployment Standards

  • Configuring runtime environments and managing dependencies
  • Exposing graph workflows through standardized API endpoints
  • Managing version control and implementing rolling updates

Conclusion and Strategic Recommendations

Requirements

  • Proficiency in foundational Python programming concepts
  • Practical experience with RESTful APIs or command-line interface tools
  • Familiarity with Large Language Model principles and basic prompt engineering techniques

Target Audience

  • Developers and software engineers initiating work in graph-based LLM orchestration
  • Prompt engineers and emerging AI specialists developing complex LLM applications for government operations
  • Data practitioners investigating LLM-driven workflow automation

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