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Course Outline
Introduction to LangGraph and Graph Concepts
- Why graphs for LLM apps: orchestration vs. simple chains
- Nodes, edges, and state in LangGraph
- Hello LangGraph: first runnable graph
State Management and Prompt Chaining
- Designing prompts as graph nodes
- Passing state between nodes and handling outputs
- Memory patterns: short-term vs. persisted context
Branching, Control Flow, and Error Handling
- Conditional routing and multi-path workflows
- Retries, timeouts, and fallback strategies
- Idempotency and safe re-runs
Tools and External Integrations
- Function/tool calling from graph nodes
- Calling REST APIs and services within the graph
- Working with structured outputs
Retrieval-Augmented Workflows
- Document ingestion and chunking basics
- 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: factuality, safety, and determinism
Packaging and Deployment Fundamentals
- Environment setup and dependency management
- Serving graphs behind APIs
- Versioning workflows and rolling updates
Summary and Next Steps
Requirements
- An understanding of basic Python programming
- Experience with REST APIs or CLI tools
- Familiarity with LLM concepts and prompt engineering fundamentals
Audience
- Developers and software engineers new to graph-based LLM orchestration
- Prompt engineers and AI newcomers building multi-step LLM apps
- Data practitioners exploring workflow automation with LLMs
14 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 3200 € + VAT*
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