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Course Outline
Introduction to Agent Builder and RAG
- Overview of Agent Builder capabilities.
- RAG fundamentals and appropriate use cases.
- Real-world applications and success stories.
Environment Setup
- Configuring the Vertex AI workspace.
- Linking search and vector stores.
- Hands-on lab: Environment preparation.
Designing Grounded Agent Workflows
- Defining agent objectives and conversation flows.
- Mapping data sources to retrieval strategies.
- Hands-on lab: Building a conversation flow.
Implementing RAG Pipelines
- Indexing documents and embeddings.
- Retriever and re-ranker patterns.
- Hands-on lab: Creating a RAG pipeline.
Integrations and Enterprise Data
- Secure connectors for internal systems.
- Data governance and access controls.
- Hands-on lab: Connecting enterprise data sources.
Testing, Evaluation, and Iteration
- Prompt testing and evaluation metrics.
- User simulation and validation strategies.
- Hands-on lab: Evaluating 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.
- Experience with cloud services and APIs.
- Familiarity with search and vector databases.
Target Audience
- Developers
- Solution architects
- Product managers
14 Hours