Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Foundations of LangGraph and Graph Theory
- The rationale for using graphs in LLM applications: orchestration versus linear chains
- Defining nodes, edges, and state within LangGraph
- Initial implementation: creating your first executable graph
State Control and Prompt Sequencing
- Modeling prompts as individual graph nodes
- Managing state transfer between nodes and processing outputs
- Memory strategies: distinguishing between short-term and persisted context
Branching, Logic Flow, and Exception Management
- Implementing conditional routing and multi-path workflows
- Configuring retries, timeouts, and fallback mechanisms
- Ensuring idempotency and safe re-execution
Tool Usage and External Connectors
- Invoking functions and tools directly from graph nodes
- Interacting with REST APIs and services within the graph structure
- Handling structured data outputs
Retrieval-Augmented Generation (RAG) Workflows
- Basics of document ingestion and text chunking
- Utilizing embeddings and vector databases (e.g., ChromaDB)
- Generating grounded responses with proper citations
Quality Assurance, Troubleshooting, and Assessment
- Developing unit-style tests for individual nodes and workflow paths
- Implementing tracing and observability features
- Validating quality metrics such as factuality, safety, and determinism
Packaging and Deployment Essentials
- Configuring environments and managing dependencies
- Exposing graphs via API endpoints
- Managing workflow versions and executing rolling updates
Recap and Future Directions
Requirements
- A solid grasp of fundamental Python programming concepts
- Practical experience with REST APIs or Command Line Interface (CLI) tools
- Basic knowledge of Large Language Model (LLM) principles and prompt engineering techniques
Target Audience
- Developers and software engineers new to graph-based LLM orchestration
- Prompt engineers and emerging AI specialists developing multi-step LLM applications
- Data practitioners investigating LLM-driven workflow automation
