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Course Outline

Introduction to Agent Builder and RAG

  • Overview of Agent Builder capabilities
  • Fundamentals of RAG and appropriate use cases
  • Real-world use cases 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: Building a conversation flow

Implementing RAG Pipelines

  • Indexing documents and generating embeddings
  • Understanding 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 controls
  • Hands-on lab: Connecting 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 procedures

Summary and Next Steps

Requirements

  • Fundamental understanding of natural language processing
  • Hands-on experience with cloud services and APIs
  • Familiarity with search engines and vector databases

Target Audience

  • Software Developers
  • Solution Architects
  • Product Managers
 14 Hours

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