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Duration 14 hours
Course Outline
Foundations of Agent Builder and RAG
- Overview of Agent Builder features
- Core RAG concepts and application scenarios
- Real-world use cases and success stories
Environment Configuration
- Setting up the Vertex AI workspace
- Linking search indices and vector stores
- Lab session: Preparing the development environment
Architecting Grounded Agent Workflows
- Establishing agent objectives and conversational logic
- Aligning data sources with retrieval methodologies
- Lab session: Constructing a conversational flow
Building RAG Pipelines
- Document indexing and embedding generation
- Strategies for retrievers and re-rankers
- Lab session: Assembling a complete RAG pipeline
Enterprise Integration and Data Management
- Implementing secure connections to internal systems
- Enforcing data governance and access permissions
- Lab session: Connecting to enterprise data repositories
Validation, Assessment, and Optimization
- Prompt testing and defining evaluation metrics
- User simulation techniques and validation approaches
- Lab session: Performance evaluation and agent tuning
Release, Surveillance, and Upkeep
- Deployment strategies and scaling factors
- Monitoring performance, relevance, and data drift
- Operational guides for updates and rollback procedures
Conclusion and Future Steps
Requirements
- Fundamental understanding of natural language processing
- Proficiency with cloud services and API interactions
- Working knowledge of search engines and vector databases
Target Audience
- Software developers
- Solution architects
- Product managers
