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Duration 14 hours
Course Outline
Introduction to Agent Builder and RAG
- Examining the core capabilities of Agent Builder
- Foundations of RAG and applicable scenarios
- Real-world use cases and success narratives
Environment Configuration
- Setting up the Vertex AI workspace
- Linking search and vector stores
- Practical lab: Preparing the environment
Architecting 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 generating embeddings
- Applying retriever and re-ranker patterns
- Practical lab: Developing a RAG pipeline
Integrations and Enterprise Data
- Securely connecting to internal systems
- Managing data governance and access permissions
- Practical lab: Linking enterprise data sources
Testing, Evaluation, and Iteration
- Testing prompts and defining evaluation metrics
- Simulating users and applying validation strategies
- Practical lab: Assessing and fine-tuning the agent
Deployment, Monitoring, and Maintenance
- Deployment strategies and scaling factors
- Tracking performance, relevance, and drift
- Operational guides for updates and rollbacks
Summary and Next Steps
Requirements
- Fundamental understanding of natural language processing
- Practical experience with cloud services and APIs
- Proficiency with search and vector databases
Target Audience
- Software developers
- Solution architects
- Product managers
