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Duration 14 hours
Course Outline
Foundations of Agent Builder and RAG
- Exploring the capabilities of Agent Builder
- Core principles of RAG and applicable scenarios
- Practical use cases and real-world success stories
Preparing the Workspace
- Setting up the Vertex AI workspace
- Linking search and vector stores
- Practical exercise: Preparing the environment
Crafting Grounded Agent Workflows
- Defining agent objectives and conversation structures
- Aligning data sources with retrieval methods
- Practical exercise: Constructing a conversation flow
Building RAG Pipelines
- Document indexing and embedding creation
- Patterns for retrievers and re-rankers
- Practical exercise: Assembling a RAG pipeline
Enterprise Integrations and Data Management
- Establishing secure connectors to internal systems
- Data governance and access control protocols
- Practical exercise: Linking enterprise data sources
Testing, Evaluation, and Iterative Improvement
- Prompt testing and assessment metrics
- User simulation and validation approaches
- Practical exercise: Evaluating and tuning the agent
Deployment, Monitoring, and Ongoing Maintenance
- Deployment strategies and scaling factors
- Tracking performance, relevance, and drift
- Operational guidelines for updates and rollbacks
Wrap-Up and Future Directions
Requirements
- Fundamental understanding of natural language processing
- Practical experience with cloud services and APIs
- Working knowledge of search and vector databases
Target Audience
- Software Developers
- Solution Architects
- Product Managers
