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Course Outline
Introduction to Agent Builder and RAG
- Exploring the capabilities of Agent Builder
- Core principles of RAG and applicable scenarios
- Real-world use cases and success stories
Environment Setup
- Setting up the Vertex AI workspace
- Establishing connections to search and vector stores
- Practical lab: Preparing the environment
Designing Grounded Agent Workflows
- Defining agent objectives and conversation structures
- Aligning data sources with retrieval strategies
- Practical lab: Constructing a conversation flow
Building RAG Pipelines
- Document indexing and embedding creation
- Patterns for retrievers and re-rankers
- Practical lab: Assembling a RAG pipeline
Integrations and Enterprise Data
- Securing connections to internal systems
- Data governance and access management
- Practical lab: Linking enterprise data sources
Testing, Evaluation, and Iteration
- Prompt testing and performance metrics
- User simulation and validation methods
- Practical lab: Evaluating and tuning the agent
Deployment, Monitoring, and Maintenance
- Deployment options and scaling factors
- Tracking performance, relevance, and drift
- Operational procedures for updates and rollbacks
Summary and Future Directions
Requirements
- Fundamental understanding of natural language processing
- Practical experience with cloud services and APIs
- Awareness of search technologies and vector databases
Target Audience
- Software developers
- Solution architects
- Product managers
14 Hours
