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Duration 14 hours
Course Outline
Introduction to Agent Builder and RAG
- Overview of Agent Builder’s capabilities.
- Core concepts of RAG and scenarios for its application.
- Real-world use cases and success stories.
Setting Up the Environment
- Configuring the Vertex AI workspace.
- Connecting to search and vector stores.
- Hands-on lab: Preparing the environment.
Designing Grounded Agent Workflows
- Defining agent objectives and conversation flows.
- Mapping data sources to retrieval strategies.
- Hands-on lab: Constructing a conversation flow.
Implementing RAG Pipelines
- Indexing documents and managing embeddings.
- Understanding retriever and re-ranker patterns.
- Hands-on lab: Creating a RAG pipeline.
Integrations and Enterprise Data
- Establishing secure connectors to internal systems.
- Data governance and access control mechanisms.
- Hands-on lab: Linking enterprise data sources.
Testing, Evaluation, and Iteration
- Conducting prompt tests and defining evaluation metrics.
- User simulation and validation methodologies.
- Hands-on lab: Evaluating and tuning the agent.
Deployment, Monitoring, and Maintenance
- Exploring deployment options and scaling considerations.
- Monitoring performance, relevance, and model drift.
- Operational playbooks for updates and rollback procedures.
Summary and Next Steps
Requirements
- Fundamental understanding of natural language processing.
- Practical experience with cloud services and APIs.
- Familiarity with search engines and vector databases.
Target Audience
- Developers.
- Solution architects.
- Product managers.
