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Duration 14 hours
Course Outline
Introduction to Agent Builder and RAG
- Exploring Agent Builder capabilities
- Core RAG concepts and their application scenarios
- Real-world use cases and success stories
Setting Up the Environment
- Configuring the Vertex AI workspace
- Linking search and vector stores
- Lab: Preparing the development environment
Designing Grounded Agent Workflows
- Defining agent objectives and conversation paths
- Aligning data sources with retrieval strategies
- Lab: Constructing a conversation flow
Implementing RAG Pipelines
- Indexing documents and generating embeddings
- Utilizing retriever and re-ranker patterns
- Lab: Building a RAG pipeline
Integrations and Enterprise Data
- Establishing secure connectors to internal systems
- Managing data governance and access controls
- Lab: Connecting enterprise data sources
Testing, Evaluation, and Iteration
- Conducting prompt tests and analyzing evaluation metrics
- Employing user simulation and validation strategies
- Lab: Evaluating and fine-tuning the agent
Deployment, Monitoring, and Maintenance
- Reviewing deployment options and scaling needs
- Tracking performance, relevance, and data drift
- Creating operational playbooks for updates and rollbacks
Summary and Next Steps
Requirements
- Fundamental understanding of natural language processing
- Practical experience with cloud services and APIs
- Knowledge of search and vector databases
Target Audience
- Developers
- Solution architects
- Product managers
