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Duration 14 hours
Course Outline
Introduction to Agent Builder and RAG
- Explore the capabilities of Agent Builder
- Core concepts of RAG and applicable scenarios
- Real-world use cases and success stories
Environment Setup
- Configuration of the Vertex AI workspace
- Establishing connections to search and vector stores
- Practical lab: preparing the environment
Designing Grounded Agent Workflows
- Defining agent objectives and conversation pathways
- Aligning data sources with retrieval strategies
- Practical lab: creating a conversation flow
Building RAG Pipelines
- Indexing documents and generating embeddings
- Applying retriever and re-ranker patterns
- Practical lab: constructing a RAG pipeline
Integrations and Enterprise Data
- Securing connectors to internal systems
- Managing data governance and access controls
- Practical lab: linking enterprise data sources
Testing, Evaluation, and Iteration
- Conducting prompt tests and analyzing evaluation metrics
- Employing user simulation and validation methods
- Practical lab: assessing and adjusting the agent
Deployment, Monitoring, and Maintenance
- Reviewing deployment options and scaling factors
- Monitoring performance, relevance, and drift
- Developing operational playbooks for updates and rollbacks
Conclusion and Future Directions
Requirements
- Fundamental understanding of natural language processing
- Proficiency with cloud services and APIs
- Working knowledge of search and vector databases
Target Audience
- Developers
- Solution architects
- Product managers
