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Duration 14 hours
Course Outline
Getting Started with Agent Builder and RAG
- Exploring the core capabilities of Agent Builder
- Essential concepts of RAG and its optimal applications
- Real-world use cases and success stories
Preparing the Development Environment
- Setting up your Vertex AI workspace
- Linking search and vector store resources
- Practical lab: preparing your environment
Architecting Grounded Agent Workflows
- Establishing agent objectives and conversation structures
- Aligning data sources with effective retrieval strategies
- Practical lab: constructing a conversation flow
Building RAG Pipelines
- Indexing documents and generating embeddings
- Utilizing retriever and re-ranker patterns
- Practical lab: assembling a RAG pipeline
Enterprise Integrations and Data Management
- Establishing secure connections to internal systems
- Implementing data governance and access controls
- Practical lab: linking enterprise data sources
Testing, Assessment, and Refinement
- Conducting prompt tests and analyzing evaluation metrics
- Applying user simulation and validation techniques
- Practical lab: assessing and fine-tuning the agent
Deployment, Oversight, and Maintenance
- Evaluating deployment options and scaling factors
- Monitoring performance, relevance, and model drift
- Developing operational playbooks for updates and rollbacks
Recap and Future Directions
Requirements
- Fundamental understanding of natural language processing
- Practical experience with cloud services and APIs
- Working knowledge of search engines and vector databases
Target Audience
- Developers
- Solution Architects
- Product Managers
