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Course Outline
Introduction to Agent Builder and RAG
- Overview of Agent Builder capabilities.
- RAG fundamentals and scenarios for application.
- Real-world use cases and success stories.
Setting Up the Environment
- Configuring the Vertex AI workspace.
- Connecting search indices and vector stores.
- Hands-on lab: Preparing the environment.
Designing Grounded Agent Workflows
- Defining agent objectives and conversation flows.
- Mapping data sources to effective retrieval strategies.
- Hands-on lab: Constructing a conversation flow.
Implementing RAG Pipelines
- Indexing documents and generating embeddings.
- Applying retriever and re-ranker patterns.
- Hands-on lab: Creating a RAG pipeline.
Integrations and Enterprise Data
- Establishing secure connectors to internal systems.
- Implementing data governance and access controls.
- Hands-on lab: Connecting enterprise data sources.
Testing, Evaluation, and Iteration
- Conducting prompt testing and analyzing evaluation metrics.
- Employing user simulation and validation strategies.
- Hands-on lab: Evaluating and tuning the agent.
Deployment, Monitoring, and Maintenance
- Exploring deployment options and scaling considerations.
- Monitoring performance, relevance, and model drift.
- Developing operational playbooks for updates and rollbacks.
Summary and Next Steps
Requirements
- Foundational 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.
14 Hours
