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Duration 14 hours
Course Outline
Foundations of Agent Builder and RAG
- Exploring the core capabilities of Agent Builder
- Key concepts of RAG and scenarios for its application
- Real-world use cases and successful implementations
Environment Configuration
- Setting up the Vertex AI workspace
- Establishing connections to search and vector stores
- Practical lab: preparing the development environment
Architecting Grounded Agent Workflows
- Establishing agent objectives and conversation structures
- Aligning data sources with effective retrieval strategies
- Practical lab: constructing a basic conversation flow
Developing RAG Pipelines
- Processing documents and generating embeddings
- Utilizing retriever and re-ranker patterns
- Practical lab: assembling a complete RAG pipeline
Enterprise Data Integration
- Creating secure connections to internal systems
- Implementing data governance and access controls
- Practical lab: linking enterprise data sources
Validation, Assessment, and Refinement
- Conducting prompt tests and analyzing evaluation metrics
- Employing user simulation and validation techniques
- Practical lab: testing and optimizing agent behavior
Deployment, Surveillance, and Upkeep
- Reviewing deployment strategies and scaling factors
- Tracking performance, relevance, and model drift
- Establishing operational guides for updates and rollbacks
Wrap-up and Future Directions
Requirements
- Fundamental understanding of natural language processing
- Practical experience with cloud platforms and APIs
- Working knowledge of search technologies and vector databases
Target Audience
- Software developers
- Solution architects
- Product managers
