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Duration 14 hours
Course Outline
Getting Started with Agent Builder and RAG
- Exploring the core features of Agent Builder
- Understanding RAG basics and applicable scenarios
- Real-world applications and case studies
Preparing the Development Environment
- Setting up the Vertex AI workspace
- Linking search engines and vector databases
- Lab exercise: initializing the environment
Structuring Grounded Agent Interactions
- Establishing agent objectives and dialogue structures
- Aligning data sources with retrieval approaches
- Lab exercise: constructing a dialogue pathway
Building RAG Pipelines
- Processing documents and generating embeddings
- Applying retriever and re-ranker architectures
- Lab exercise: assembling a RAG workflow
Enterprise Integrations and Data Management
- Establishing secure links to internal systems
- Implementing data governance and permission controls
- Lab exercise: interfacing with enterprise data
Validation, Assessment, and Optimization
- Testing prompts and defining performance indicators
- Simulating user interactions and verifying accuracy
- Lab exercise: tuning and evaluating the agent
Launch, Oversight, and Upkeep
- Reviewing release strategies and scalability factors
- Tracking system health, relevance, and drift
- Developing protocols for updates and reversals
Wrap-up and Future Directions
Requirements
- Fundamental understanding of natural language processing
- Practical experience with cloud platforms and API integration
- Working knowledge of search engines and vector databases
Target Audience
- Software developers
- Solution architects
- Product managers
