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Duration 14 hours
Course Outline
Introduction to Agent Builder and RAG
- Overview of Agent Builder features
- Basics of RAG and its application scenarios
- Real-world use cases and success stories
Environment Setup
- Configuring the Vertex AI workspace
- Connecting search and vector stores
- Hands-on lab: preparing the environment
Designing Grounded Agent Workflows
- Defining agent objectives and conversation flows
- Aligning data sources with retrieval strategies
- Hands-on lab: creating a conversation flow
Implementing RAG Pipelines
- Indexing documents and generating embeddings
- Applying retriever and re-ranker patterns
- Hands-on lab: constructing a RAG pipeline
Integrations and Enterprise Data
- Secure connections to internal systems
- Data governance and access control mechanisms
- Hands-on lab: linking enterprise data sources
Testing, Evaluation, and Iteration
- Prompt testing and performance evaluation metrics
- User simulation and validation techniques
- Hands-on lab: assessing and tuning the agent
Deployment, Monitoring, and Maintenance
- Deployment options and scaling factors
- Monitoring performance, relevance, and drift
- Operational playbooks for updates and rollbacks
Summary and Next Steps
Requirements
- Foundational knowledge of natural language processing
- Experience with cloud services and APIs
- Understanding of search and vector databases
Target Audience
- Developers
- Solution architects
- Product managers
