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Duration 14 hours
Course Outline
Introduction to Agent Builder and RAG
- Exploration of Agent Builder features
- RAG basics and applicable scenarios
- Real-world use cases and success examples
Environment Configuration
- Setting up the Vertex AI workspace
- Linking search and vector stores
- Practical lab: preparing the environment
Constructing Grounded Agent Workflows
- Establishing agent objectives and dialogue structures
- Aligning data sources with retrieval methods
- Practical lab: creating a dialogue flow
Building RAG Pipelines
- Document indexing and embedding processes
- Reversal and re-ranking mechanisms
- Practical lab: assembling a RAG pipeline
Integrations and Enterprise Data
- Secure links to internal systems
- Data governance and permission management
- Practical lab: connecting enterprise data sources
Testing, Evaluation, and Refinement
- Prompt validation and performance metrics
- User simulation and verification techniques
- Practical lab: assessing and tuning the agent
Deployment, Monitoring, and Upkeep
- Deployment strategies and scalability factors
- Tracking performance, relevance, and data drift
- Operational guides for updates and rollbacks
Conclusion and Future Directions
Requirements
- Foundational understanding of natural language processing
- Proficiency with cloud services and APIs
- Knowledge of search and vector databases
Target Audience
- Software Developers
- Solution Architects
- Product Managers
