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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
- Real-world use cases and success stories
Environment Setup
- Configuring the Vertex AI workspace
- Linking search and vector stores
- Hands-on lab: Preparing the development environment
Designing Grounded Agent Workflows
- Defining agent objectives and dialogue flows
- Aligning data sources with retrieval strategies
- Hands-on lab: Constructing a conversation flow
Building RAG Pipelines
- Indexing documents and managing embeddings
- Utilizing retriever and re-ranker patterns
- Hands-on lab: Assembling a RAG pipeline
Integrations and Enterprise Data
- Establishing secure connectors to internal systems
- Managing data governance and access controls
- Hands-on lab: Connecting enterprise data sources
Testing, Evaluation, and Iteration
- Conducting prompt tests and analyzing evaluation metrics
- Applying user simulation and validation methods
- Hands-on lab: Evaluating and tuning the agent
Deployment, Monitoring, and Maintenance
- Reviewing deployment options and scaling factors
- Monitoring performance, relevance, and drift
- Creating operational playbooks for updates and rollbacks
Summary and Next Steps
Requirements
- Fundamental understanding of natural language processing
- Practical experience with cloud services and APIs
- Working knowledge of search engines and vector databases
Target Audience
- Developers
- Solution architects
- Product managers
