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
- The rationale for using graphs in LLM apps: orchestration versus simple chains.
- Core components: nodes, edges, and state in LangGraph.
- Hello LangGraph: creating your first runnable graph.
State Management and Prompt Chaining
- Designing prompts as graph nodes.
- Passing state between nodes and managing outputs.
- Memory patterns: distinguishing between short-term and persisted context.
Branching, Control Flow, and Error Handling
- Conditional routing and multi-path workflows.
- Strategies for retries, timeouts, and fallbacks.
- Ensuring idempotency and safe re-execution.
Tools and External Integrations
- Invoking functions and tools from within graph nodes.
- Calling REST APIs and external services within the graph.
- Managing and utilizing structured outputs.
Retrieval-Augmented Workflows
- Basics of document ingestion and chunking.
- Utilizing embeddings and vector stores (e.g., ChromaDB).
- Generating grounded answers with citations.
Testing, Debugging, and Evaluation
- Implementing unit-style tests for nodes and paths.
- Techniques for tracing and observability.
- Conducting quality checks for factuality, safety, and determinism.
Packaging and Deployment Fundamentals
- Setting up environments and managing dependencies.
- Serving graphs via APIs.
- Versioning workflows and executing rolling updates.
Summary and Next Steps
Requirements
- Foundational understanding of Python programming.
- Practical experience with REST APIs or CLI tools.
- Familiarity with LLM concepts and fundamental principles of prompt engineering.
Target Audience
- Developers and software engineers new to graph-based LLM orchestration.
- Prompt engineers and AI beginners developing multi-step LLM applications.
- Data practitioners exploring workflow automation leveraging LLMs.
14 Hours
