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Course Outline
Introduction to Agent Builder and RAG
- Examining the core capabilities of Agent Builder
- Understanding RAG fundamentals and applicable scenarios
- Reviewing real-world use cases and success stories
Environment Configuration
- Setting up the Vertex AI workspace
- Linking search and vector stores
- Practical lab: Preparing the environment
Architecting Grounded Agent Workflows
- Establishing agent objectives and conversation paths
- Aligning data sources with retrieval strategies
- Practical lab: Developing a conversation flow
Building RAG Pipelines
- Indexing documents and generating embeddings
- Utilizing retriever and re-ranker patterns
- Practical lab: Constructing a RAG pipeline
Enterprise Data Integration
- Establishing secure connectors to internal systems
- Managing data governance and access permissions
- Practical lab: Connecting enterprise data sources
Testing, Assessment, and Refinement
- Conducting prompt testing and analyzing evaluation metrics
- Applying user simulation and validation techniques
- Practical lab: Evaluating and tuning agent performance
Deployment, Monitoring, and Maintenance
- Exploring deployment options and scaling factors
- Tracking performance, relevance, and model drift
- Implementing operational playbooks for updates and rollback
Conclusion and Future Path
Requirements
- Fundamental understanding of natural language processing
- Practical experience with cloud services and APIs
- Working knowledge of search and vector databases
Intended Audience
- Software Developers
- Solution Architects
- Product Managers
14 Hours
