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.
Course Outline
Introduction to LangGraph and Graph Concepts
- Utilizing graphs for LLM applications: orchestrating complex flows versus simple chains
- Understanding nodes, edges, and state within LangGraph
- Getting started with LangGraph: building your first runnable graph
State Management and Prompt Chaining
- Structuring prompts as graph nodes
- Managing state transitions between nodes and handling outputs
- Implementing memory patterns: distinguishing between short-term and persisted context
Branching, Control Flow, and Error Handling
- Conditional routing and designing multi-path workflows
- Strategies for retries, timeouts, and fallback mechanisms
- Ensuring idempotency and safe re-execution
Tools and External Integrations
- Invoking functions and tools from graph nodes
- Interacting with REST APIs and external services within the graph
- Working effectively with structured outputs
Retrieval-Augmented Workflows
- Basics of document ingestion and chunking
- Understanding embeddings and vector stores (e.g., ChromaDB)
- Providing grounded answers with proper citations
Testing, Debugging, and Evaluation
- Conducting unit-style tests for nodes and pathways
- Implementing tracing and observability features
- Performing quality checks for factuality, safety, and determinism
Packaging and Deployment Fundamentals
- Setting up environments and managing dependencies
- Deploying graphs via APIs
- Managing workflow versioning and executing rolling updates
Summary and Next Steps
Requirements
- Foundational understanding of Python programming
- Experience with REST APIs or CLI tools
- Familiarity with core LLM concepts and prompt engineering fundamentals
Target Audience
- Developers and software engineers new to graph-based LLM orchestration
- Prompt engineers and AI newcomers developing multi-step LLM applications
- Data practitioners investigating workflow automation using LLMs
14 Hours
