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Course Outline
Introduction to Agent Builder and RAG
- Overview of Agent Builder's features
- Core concepts of RAG and applicable scenarios
- Real-world use cases and success stories
Environment Configuration
- Setting up the Vertex AI workspace
- Linking search and vector stores
- Hands-on lab: preparing the environment
Designing Grounded Agent Workflows
- Establishing agent objectives and dialogue flows
- Aligning data sources with retrieval strategies
- Hands-on lab: constructing a conversation flow
Building 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 connections to internal systems
- Implementing data governance and access controls
- Hands-on lab: connecting enterprise data sources
Testing, Evaluation, and Iteration
- Conducting prompt tests and defining evaluation metrics
- Employing user simulation and validation methods
- Hands-on lab: evaluating and tuning the agent
Deployment, Monitoring, and Maintenance
- Deployment strategies and scaling factors
- Monitoring performance, relevance, and data drift
- Operational procedures for updates and rollbacks
Summary and Future Steps
Requirements
- Fundamental understanding of natural language processing
- Practical experience with cloud services and APIs
- Awareness of search and vector databases
Target Audience
- Developers
- Solution architects
- Product managers
14 Hours
