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Duration 14 hours
Course Outline
Introduction to Agent Builder and RAG
- Overview of Agent Builder features
- Core RAG concepts and ideal use cases
- Real-world applications and success stories
Setting Up the Environment
- Configuring your Vertex AI workspace
- Linking search and vector stores
- Hands-on lab: preparing the environment
Designing Grounded Agent Workflows
- Defining agent objectives and dialogue flows
- Aligning data sources with retrieval strategies
- Hands-on lab: constructing a conversation flow
Implementing RAG Pipelines
- Indexing documents and generating embeddings
- Utilising retriever and re-ranker patterns
- Hands-on lab: building a RAG pipeline
Integrations and Enterprise Data
- Establishing secure connectors to internal systems
- Managing data governance and access controls
- Hands-on lab: connecting enterprise data sources
Testing, Evaluation, and Iteration
- Prompt testing and defining evaluation metrics
- User simulation and validation approaches
- Hands-on lab: assessing and tuning the agent
Deployment, Monitoring, and Maintenance
- Exploring deployment options and scaling factors
- Tracking performance, relevance, and drift
- Creating operational playbooks for updates and rollbacks
Summary and Next Steps
Requirements
- Foundational understanding of natural language processing
- Hands-on experience with cloud services and APIs
- Working knowledge of search and vector databases
Target Audience
- Developers
- Solution architects
- Product managers
