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Duration 14 hours
Course Outline
Foundations of Agent Builder and RAG
- Exploring the capabilities of Agent Builder
- Core concepts of RAG and appropriate use cases
- Real-world applications and success stories
Environment Configuration
- Setting up the Vertex AI workspace
- Linking search and vector store components
- Practical lab: preparing the development environment
Architecting Grounded Agent Workflows
- Establishing agent objectives and dialogue structures
- Aligning data sources with retrieval strategies
- Practical lab: constructing a conversation flow
Building RAG Pipelines
- Document indexing and embedding generation
- Applying retriever and re-ranker patterns
- Practical lab: developing a functional RAG pipeline
Enterprise Integration & Data Management
- Establishing secure connections to internal systems
- Implementing data governance and access protocols
- Practical lab: linking enterprise data repositories
Quality Assurance & Iterative Improvement
- Conducting prompt tests and analyzing evaluation metrics
- Employing user simulation and validation techniques
- Practical lab: assessing and optimizing agent behavior
Release, Surveillance, & Upkeep
- Evaluating deployment strategies and scalability
- Tracking performance, relevance, and data drift
- Developing operational guides for updates and rollbacks
Recap & Future Directions
Requirements
- Fundamental understanding of natural language processing
- Proficiency with cloud services and API interactions
- Knowledge of search engines and vector databases
Target Audience
- Software Developers
- Solution Architects
- Product Managers
