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Course Outline
Introduction to Agent Builder and RAG Concepts
- Exploration of Agent Builder's core capabilities
- Foundations of RAG and optimal application scenarios
- Real-world use cases and success stories
Environment Setup and Configuration
- Configuring the Vertex AI workspace
- Linking search engines and vector stores
- Practical lab: Preparing the development environment
Architecting Grounded Agent Workflows
- Defining agent objectives and conversational pathways
- Aligning data sources with retrieval methodologies
- Practical lab: Constructing a conversational flow
Building RAG Pipelines
- Document indexing and embedding strategies
- Patterns for retrievers and re-rankers
- Practical lab: Developing a RAG pipeline
Enterprise Integration and Data Management
- Establishing secure connections to internal systems
- Data governance and access control mechanisms
- Practical lab: Linking enterprise data sources
Testing, Evaluation, and Iterative Improvement
- Prompt testing techniques and evaluation metrics
- User simulation methods and validation approaches
- Practical lab: Assessing and tuning agent performance
Deployment, Monitoring, and Ongoing Maintenance
- Deployment strategies and scalability factors
- Tracking performance, relevance, and data drift
- Operational protocols for updates and rollback procedures
Course Summary and Future Directions
Requirements
- Foundational understanding of natural language processing
- Practical experience with cloud services and API interactions
- Acquaintance with search engines and vector databases
Intended Audience
- Software Developers
- Solution Architects
- Product Managers
14 Hours
