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Course Outline

Understanding Agent Builder and RAG

  • Introduction to Agent Builder functionalities
  • Core concepts of RAG and applicable scenarios
  • Real-world use cases and success examples

Environment Configuration

  • Setting up the Vertex AI workspace
  • Linking search engines and vector stores
  • Practical lab: preparing the environment

Designing Grounded Agent Workflows

  • Establishing agent objectives and dialogue flows
  • Aligning data sources with retrieval strategies
  • Practical lab: constructing a conversation flow

Building RAG Pipelines

  • Document indexing and embedding generation
  • Utilizing retriever and re-ranker techniques
  • Practical lab: assembling a RAG pipeline

Integrations and Enterprise Data Management

  • Secure connections to internal systems
  • Data governance and access permissions
  • Practical lab: linking enterprise data sources

Testing, Assessment, and Refinement

  • Conducting prompt tests and applying evaluation metrics
  • Strategies for user simulation and validation
  • Practical lab: assessing and optimizing agent behavior

Deployment, Monitoring, and Maintenance

  • Deployment strategies and scaling factors
  • Tracking performance, relevance, and data drift
  • Operational guidelines for updates and rollback procedures

Conclusion and Future Steps

Requirements

  • Foundational understanding of natural language processing
  • Practical experience with cloud services and APIs
  • Familiarity with search technologies and vector databases

Target Audience

  • Software developers
  • Solution architects
  • Product managers
 14 Hours

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