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Duration 14 hours
Course Outline
Introduction to Agent Builder and RAG
- Overview of Agent Builder's core capabilities
- Foundations of RAG and applicable scenarios
- Real-world use cases and success stories
Environment Setup
- Configuring your Vertex AI workspace
- Establishing connections to search and vector stores
- Hands-on lab: Preparing the development environment
Architecting Grounded Agent Workflows
- Defining agent objectives and dialogue structures
- Aligning data sources with retrieval strategies
- Hands-on lab: Constructing a conversation flow
Building RAG Pipelines
- Processing documents and generating embeddings
- Implementing retriever and re-ranker patterns
- Hands-on lab: Developing a RAG pipeline
Enterprise Data Integration
- Establishing secure connectors to internal systems
- Implementing data governance and access controls
- Hands-on lab: Linking enterprise data sources
Testing, Evaluation, and Refinement
- Conducting prompt tests and analyzing evaluation metrics
- Applying user simulation and validation techniques
- Hands-on lab: Assessing and tuning agent performance
Deployment, Monitoring, and Maintenance
- Exploring deployment strategies and scaling requirements
- Tracking performance, relevance, and data drift
- Creating operational playbooks for updates and rollbacks
Conclusion and Path Forward
Requirements
- Fundamental understanding of natural language processing
- Practical experience with cloud services and API interactions
- Knowledge of search engines and vector databases
Target Audience
- Software developers
- Solution architects
- Product managers
