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Duration 14 hours
Course Outline
Introduction to Agent Builder and RAG
- Overview of Agent Builder capabilities
- Foundations of RAG and scenarios for its application
- Real-world use cases and success stories
Preparing the Environment
- Configuring the Vertex AI workspace
- Linking search and vector stores
- Practical lab: setting up 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 processes
- Patterns for retrievers and re-rankers
- Practical lab: developing a RAG pipeline
Integrations and Enterprise Data
- Secure connections to internal systems
- Data governance and access control mechanisms
- Practical lab: connecting 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 options and scalability considerations
- Monitoring performance, relevance, and model drift
- Operational guides for updates and rollbacks
Conclusion and Next Steps
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
