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Duration 14 hours
Course Outline
Introduction to Agent Builder and RAG
- Overview of Agent Builder’s core capabilities
- Foundations of RAG and its optimal use cases
- Real-world applications and success stories
Environment Setup
- Configuration of the Vertex AI workspace
- Integration of search and vector store components
- Practical lab: Preparing the development environment
Architecting Grounded Agent Workflows
- Defining agent objectives and conversational paths
- Aligning data sources with retrieval strategies
- Practical lab: Constructing a conversational flow
Building RAG Pipelines
- Indexing documents and generating embeddings
- Exploring retriever and re-ranker patterns
- Practical lab: Assembling a RAG pipeline
Integrations and Enterprise Data
- Establishing secure connections to internal systems
- Implementing data governance and access controls
- Practical lab: Linking enterprise data sources
Testing, Assessment, and Refinement
- Conducting prompt testing and defining evaluation metrics
- Applying user simulation and validation techniques
- Practical lab: Evaluating and tuning agent performance
Deployment, Monitoring, and Upkeep
- Reviewing deployment options and scalability factors
- Tracking performance, relevance, and drift
- Developing operational playbooks for updates and rollbacks
Conclusion and Future Directions
Requirements
- Fundamental understanding of natural language processing.
- Practical experience with cloud services and APIs.
- Knowledge of search technologies and vector databases.
Target Audience
- Developers
- Solution Architects
- Product Managers
