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 Duration 14 hours

Course Outline

Introduction to Agent Builder and RAG

  • Overview of Agent Builder’s core capabilities
  • Foundations of RAG and scenarios for its application
  • Real-world use cases and success stories

Environment Setup

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

Designing Grounded Agent Workflows

  • Defining agent objectives and conversation structures
  • Aligning data sources with retrieval strategies
  • Hands-on lab: constructing a conversation flow

Implementing RAG Pipelines

  • Indexing documents and generating embeddings
  • Applying retriever and re-ranker patterns
  • Hands-on lab: building a functional RAG pipeline

Integrations and Enterprise Data

  • Establishing secure connectors to internal systems
  • Implementing data governance and access controls
  • Hands-on lab: integrating enterprise data sources

Testing, Evaluation, and Iteration

  • Conducting prompt tests and analyzing evaluation metrics
  • Employing user simulation and validation strategies
  • Hands-on lab: assessing and tuning the agent

Deployment, Monitoring, and Maintenance

  • Exploring deployment options and scaling considerations
  • Monitoring performance, relevance, and data drift
  • Creating operational playbooks for updates and rollbacks

Summary and Next Steps

Requirements

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

Target Audience

  • Software Developers
  • Solution Architects
  • Product Managers

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