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Course Outline

Introduction to Agent Builder and RAG

  • Overview of Agent Builder capabilities
  • RAG fundamentals and appropriate use cases
  • Real-world applications and success stories

Environment Setup

  • Configuring the Vertex AI workspace
  • Connecting search and vector stores
  • Hands-on lab: preparing the environment

Designing Grounded Agent Workflows

  • Defining agent objectives and conversation flows
  • Mapping data sources to retrieval strategies
  • Hands-on lab: constructing a conversation flow

Implementing RAG Pipelines

  • Indexing documents and managing embeddings
  • Applying retriever and re-ranker patterns
  • Hands-on lab: creating a RAG pipeline

Integrations and Enterprise Data

  • Secure connectors for internal systems
  • Data governance and access control mechanisms
  • Hands-on lab: linking enterprise data sources

Testing, Evaluation, and Iteration

  • Prompt testing and evaluation metrics
  • User simulation and validation strategies
  • Hands-on lab: evaluating and tuning the agent

Deployment, Monitoring, and Maintenance

  • Deployment options and scaling considerations
  • Monitoring performance, relevance, and data drift
  • Operational playbooks for updates and rollback

Summary and Next Steps

Requirements

  • Fundamental understanding of natural language processing
  • Experience with cloud services and APIs
  • Familiarity with search and vector databases

Target Audience

  • Developers
  • Solution architects
  • Product managers
 14 Hours

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