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Duration 14 hours
Course Outline
Introduction to Agent Builder and RAG
- Examining the core features of Agent Builder
- Understanding RAG concepts and ideal application scenarios
- Reviewing use cases and real-world success examples
Preparing the Development Environment
- Setting up the Vertex AI workspace
- Establishing connections to search and vector stores
- Practical lab: setting up the environment
Creating Grounded Agent Workflows
- Establishing agent objectives and dialogue paths
- Aligning data sources with appropriate retrieval methods
- Practical lab: constructing a conversation flow
Building RAG Pipelines
- Processing document indexing and embedding generation
- Applying retriever and re-ranker design patterns
- Practical lab: developing a RAG pipeline
Integrating Enterprise Data
- Implementing secure connectors for internal systems
- Managing data governance and access permissions
- Practical lab: linking enterprise data sources
Testing, Assessment, and Refinement
- Conducting prompt testing and defining evaluation criteria
- Applying user simulation and validation techniques
- Practical lab: testing and optimizing the agent
Deployment, Oversight, and Upkeep
- Evaluating deployment strategies and scalability factors
- Tracking performance, relevance, and model drift
- Creating operational plans for updates and rollback procedures
Wrap-up and Future Directions
Requirements
- Foundational 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
