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Duration 14 hours
Course Outline
Introduction to Agent Builder and RAG
- Overview of Agent Builder capabilities
- Core RAG concepts and application scenarios
- Use cases and success stories
Setting Up the Environment
- Configuring the Vertex AI workspace
- Linking search and vector stores
- Practical lab: preparing the environment
Designing Grounded Agent Workflows
- Establishing agent goals and conversation flows
- Aligning data sources with retrieval strategies
- Practical lab: constructing a conversation flow
Implementing RAG Pipelines
- Indexing documents and generating embeddings
- Retriever and re-ranker patterns
- Practical lab: creating a RAG pipeline
Integrations and Enterprise Data
- Secure connectors for internal systems
- Data governance and access controls
- Practical lab: linking enterprise data sources
Testing, Evaluation, and Iteration
- Prompt testing and evaluation metrics
- User simulation and validation strategies
- Practical lab: assessing and tuning the agent
Deployment, Monitoring, and Maintenance
- Deployment options and scaling considerations
- Monitoring performance, relevance, and drift
- Operational playbooks for updates and rollback
Summary and Next Steps
Requirements
- Fundamental understanding of natural language processing
- Practical experience with cloud services and APIs
- Proficiency with search and vector databases
Target Audience
- Developers
- Solution architects
- Product managers
