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Duration 14 hours
Course Outline
Introduction to Agent Builder and RAG
- Overview of Agent Builder features and capabilities
- Core principles of RAG and applicable scenarios
- Real-world use cases and case studies
Environment Configuration
- Setting up the Vertex AI workspace
- Linking search engines and vector stores
- Practical exercise: Preparing the environment
Creating Grounded Agent Workflows
- Establishing agent objectives and conversation pathways
- Aligning data sources with retrieval strategies
- Practical exercise: Developing a conversation flow
Building RAG Pipelines
- Processing documents and generating embeddings
- Utilizing retriever and re-ranker architectures
- Practical exercise: Constructing a RAG pipeline
Integrations and Enterprise Data
- Establishing secure connections to internal systems
- Implementing data governance and access management
- Practical exercise: Connecting enterprise data sources
Testing, Evaluation, and Optimization
- Conducting prompt tests and analyzing performance metrics
- Employing user simulation and validation methods
- Practical exercise: Assessing and adjusting agent behavior
Deployment, Monitoring, and Maintenance
- Exploring deployment options and scaling strategies
- Tracking performance, relevance, and model drift
- Operational guides for updates and rollbacks
Conclusion and Future Directions
Requirements
- Fundamental understanding of natural language processing
- Practical experience with cloud services and APIs
- Acquaintance with search engines and vector databases
Target Audience
- Software developers
- Solution architects
- Product managers
