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Duration 14 hours
Course Outline
Introduction to Agent Builder and RAG
- Exploring the capabilities of Agent Builder
- RAG fundamentals and scenarios for application
- Real-world use cases and success stories
Preparing the Environment
- Setting up the Vertex AI workspace
- Linking search and vector stores
- Practical lab: preparing the environment
Creating Grounded Agent Workflows
- Establishing agent objectives and conversation paths
- Aligning data sources with retrieval strategies
- Practical lab: constructing a conversation flow
Building RAG Pipelines
- Indexing documents and creating embeddings
- Patterns for retrievers and re-rankers
- Practical lab: developing a RAG pipeline
Integrations and Enterprise Data
- Secure connections to internal systems
- Data governance and access management
- Practical lab: linking enterprise data sources
Testing, Assessment, and Refinement
- Prompt testing and evaluation indicators
- Strategies for user simulation and validation
- Practical lab: assessing and tuning the agent
Deployment, Monitoring, and Upkeep
- Deployment choices and scalability factors
- Tracking performance, relevance, and drift
- Operational guides for updates and rollbacks
Recap and Future Directions
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
