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Course Outline
Introduction to Agent Builder and RAG
- Overview of Agent Builder’s capabilities
- RAG fundamentals and appropriate use cases
- Real-world applications and success stories
Environment Configuration
- Setting up the Vertex AI workspace
- Linking search engines and vector stores
- Hands-on lab: Preparing the environment
Architecting Grounded Agent Workflows
- Defining agent objectives and conversation paths
- Aligning data sources with retrieval strategies
- Hands-on lab: Constructing a conversation flow
Building RAG Pipelines
- Indexing documents and generating embeddings
- Patterns for retrievers and re-rankers
- Hands-on lab: Developing a RAG pipeline
Integrations and Enterprise Data
- Secure connections to internal systems
- Data governance and access control mechanisms
- Hands-on lab: Connecting enterprise data sources
Testing, Evaluation, and Iteration
- Prompt testing and assessment metrics
- User simulation and validation techniques
- Hands-on lab: Evaluating and tuning the agent
Deployment, Monitoring, and Maintenance
- Deployment options and scalability considerations
- Tracking performance, relevance, and drift
- Operational guidelines for updates and rollbacks
Wrap-up 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
- Software Developers
- Solution Architects
- Product Managers
14 Hours
