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Duration 14 hours
Course Outline
Introduction to Agent Builder and RAG
- Overview of Agent Builder’s capabilities
- Core concepts of RAG and applicable scenarios
- Real-world use cases and case studies
Environment Setup
- Configuration of the Vertex AI workspace
- Linking search and vector stores
- Hands-on lab: preparing the environment
Architecting Grounded Agent Workflows
- Defining agent objectives and dialogue flows
- Aligning data sources with retrieval strategies
- Hands-on lab: constructing a dialogue flow
Building RAG Pipelines
- Indexing documents and generating embeddings
- Patterns for retrievers and re-rankers
- Hands-on lab: developing a RAG pipeline
Integrations and Enterprise Data
- Secure connections to internal systems
- Data governance and access management
- Hands-on lab: linking enterprise data sources
Testing, Assessment, and Iteration
- Prompt testing and evaluation metrics
- Simulating user interactions and validation methods
- Hands-on lab: assessing and optimizing the agent
Deployment, Monitoring, and Upkeep
- Deployment choices and scaling factors
- Tracking performance, relevance, and drift
- Operational guides for updates and rollbacks
Conclusion and Future Actions
Requirements
- Fundamental understanding of natural language processing
- Practical experience with cloud services and APIs
- Knowledge of search and vector databases
Target Audience
- Developers
- Solution architects
- Product managers
