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Duration 14 hours
Course Outline
Foundations of Agent Builder and RAG
- Capabilities and features of Agent Builder.
- Core concepts of RAG and applicable scenarios.
- Real-world use cases and success narratives.
Environment Configuration
- Setting up the Vertex AI workspace.
- Establishing connections to search and vector stores.
- Practical lab: Preparing the development environment.
Architecting Grounded Agent Workflows
- Defining agent objectives and interaction flows.
- Aligning data sources with retrieval strategies.
- Practical lab: Constructing a conversation flow.
Building RAG Pipelines
- Document indexing and embedding generation.
- Patterns for retrievers and re-rankers.
- Practical lab: Assembling a RAG pipeline.
Enterprise Data Integration
- Establishing secure connections to internal systems.
- Managing data governance and access permissions.
- Practical lab: Linking enterprise data sources.
Testing, Assessment, and Refinement
- Conducting prompt tests and defining evaluation metrics.
- Strategies for user simulation and validation.
- Practical lab: Evaluating and tuning the agent.
Deployment, Oversight, and Maintenance
- Deployment strategies and scaling factors.
- Monitoring key performance indicators, relevance, and drift.
- Operational procedures for updates and rollbacks.
Conclusion and Future Directions
Requirements
- Fundamental understanding of natural language processing.
- Practical experience with cloud platforms and API interactions.
- Working knowledge of search technologies and vector databases.
Target Audience
- Software Developers
- Solution Architects
- Product Managers
