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Duration 14 hours
Course Outline
Introduction to Agent Builder and RAG
- Overview of Agent Builder's capabilities
- Core principles of RAG and applicable scenarios
- Real-world use cases and success stories
Preparing the Environment
- Setting up the Vertex AI workspace
- Linking search and vector stores
- Practical lab: environment setup
Designing Grounded Agent Workflows
- Defining agent objectives and conversational flows
- Aligning data sources with retrieval strategies
- Practical lab: constructing a conversation flow
Building RAG Pipelines
- Processing documents and generating embeddings
- Utilizing retriever and re-ranker patterns
- Practical lab: assembling a RAG pipeline
Integrations and Enterprise Data
- Establishing secure connections to internal systems
- Managing data governance and access controls
- Practical lab: connecting to enterprise data sources
Testing, Evaluation, and Iteration
- Conducting prompt tests and analyzing evaluation metrics
- Employing user simulation and validation strategies
- Practical lab: assessing and optimizing the agent
Deployment, Monitoring, and Maintenance
- Exploring deployment options and scalability considerations
- Monitoring performance, relevance, and data drift
- Establishing operational playbooks for updates and rollbacks
Conclusion and Future Steps
Requirements
- Fundamental understanding of natural language processing
- Proficiency with cloud services and APIs
- Knowledge of search and vector databases
Audience
- Developers
- Solution architects
- Product managers
