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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