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Duration 14 hours
Course Outline
Foundations of Agent Builder and RAG
- Overview of Agent Builder's core capabilities
- Core RAG concepts and applicable scenarios
- Real-world use cases and success stories
Environment Setup
- Configuring the Vertex AI workspace
- Linking search and vector store services
- Practical lab: preparing the working environment
Architecting Grounded Agent Workflows
- Establishing agent objectives and dialogue structures
- Aligning data sources with appropriate retrieval strategies
- Practical lab: constructing a conversational flow
Building RAG Pipelines
- Processing documents and generating embeddings
- Applying retriever and re-ranker methodologies
- Practical lab: assembling a RAG pipeline
Enterprise Integration and Data Handling
- Establishing secure connections to internal systems
- Managing data governance and access permissions
- Practical lab: connecting corporate data sources
Testing, Assessment, and Optimization
- Validating prompts and defining evaluation metrics
- Employing user simulation and verification techniques
- Practical lab: assessing and fine-tuning the agent
Deployment, Surveillance, and Upkeep
- Exploring deployment models and scalability factors
- Tracking performance, relevance, and data drift
- Developing operational playbooks for updates and rollbacks
Conclusions and Path Forward
Requirements
- Foundational understanding of natural language processing
- Proficiency with cloud services and APIs
- Experience with search engines and vector databases
Target Audience
- Software Developers
- Solution Architects
- Product Managers
