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
- Why use graphs for LLM applications: orchestration versus simple chains.
- Nodes, edges, and state within LangGraph.
- Hello LangGraph: building the first runnable graph.
State Management and Prompt Chaining
- Designing prompts as graph nodes.
- Passing state between nodes and handling outputs.
- Memory patterns: distinguishing between short-term and persisted context.
Branching, Control Flow, and Error Handling
- Conditional routing and multi-path workflows.
- Retries, timeouts, and fallback strategies.
- Ensuring idempotency and safe re-runs.
Tools and External Integrations
- Invoking functions/tools from graph nodes.
- Calling REST APIs and services within the graph.
- Working with structured outputs.
Retrieval-Augmented Workflows
- Basics of document ingestion and chunking.
- Embeddings and vector stores (e.g., ChromaDB).
- Grounded answering with citations.
Testing, Debugging, and Evaluation
- Unit-style tests for nodes and paths.
- Tracing and observability.
- Quality checks: ensuring factuality, safety, and determinism.
Packaging and Deployment Fundamentals
- Environment setup and dependency management.
- Serving graphs via APIs.
- Versioning workflows and implementing rolling updates.
Summary and Next Steps
Requirements
- A foundational understanding of Python programming.
- Experience with REST APIs or CLI tools.
- Familiarity with LLM concepts and the fundamentals of prompt engineering.
Audience
- Developers and software engineers new to graph-based LLM orchestration.
- Prompt engineers and AI beginners building multi-step LLM applications.
- Data practitioners exploring workflow automation with LLMs.
14 Hours
