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Duration 14 hours
Course Outline
Introduction to Agent Builder and RAG
- Overview of Agent Builder features and capabilities
- Basics of RAG and applicable scenarios
- Real-world use cases and case studies
Environment Setup
- Setting up the Vertex AI workspace
- Linking search engines and vector stores
- Practical lab: Preparing the environment
Creating Grounded Agent Workflows
- Defining agent objectives and dialogue flows
- Aligning data sources with retrieval methods
- Practical lab: Developing a conversation flow
Building RAG Pipelines
- Document indexing and embedding processes
- Retriever and re-ranker architectural patterns
- Practical lab: Constructing a RAG pipeline
Integrations and Enterprise Data
- Establishing secure links to internal systems
- Data governance and access control mechanisms
- Practical lab: Connecting enterprise data sources
Testing, Assessment, and Iteration
- Prompt testing and performance metrics
- User simulation and validation approaches
- Practical lab: Assessing and tuning the agent
Deployment, Monitoring, and Maintenance
- Deployment strategies and scalability factors
- Tracking performance, relevance, and data drift
- Operational guides for updates and rollbacks
Recap and Future Steps
Requirements
- Fundamental understanding of natural language processing
- Practical experience with cloud services and APIs
- Knowledge of search engines and vector databases
Target Audience
- Developers
- Solution architects
- Product managers
