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Course Outline
Introduction to Agent Builder and RAG
- Key capabilities of Agent Builder
- Core principles of RAG and applicable scenarios
- Real-world use cases and success stories
Environment Setup
- Configuring the Vertex AI workspace
- Linking search and vector stores
- Practical lab: preparing the environment
Designing Grounded Agent Workflows
- Establishing agent objectives and conversation paths
- Aligning data sources with retrieval strategies
- Practical lab: developing a conversation flow
Building RAG Pipelines
- Document indexing and embedding generation
- Patterns for retrievers and re-rankers
- Practical lab: constructing a RAG pipeline
Integrations and Enterprise Data
- Secure connections to internal systems
- Data governance and access management
- Practical lab: linking enterprise data sources
Testing, Evaluation, and Iteration
- Prompt testing and assessment metrics
- Strategies for user simulation and validation
- Practical lab: evaluating and tuning the agent
Deployment, Monitoring, and Maintenance
- Deployment methods and scaling factors
- Tracking performance, relevance, and drift
- Operational guides for updates and rollbacks
Summary and Future Directions
Requirements
- Fundamental understanding of natural language processing
- Experience with cloud services and APIs
- Working knowledge of search and vector databases
Target Audience
- Developers
- Solution Architects
- Product Managers
14 Hours
