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Course Outline

Introduction to Agent Builder and RAG

  • Overview of Agent Builder capabilities.
  • Core concepts of RAG and its 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 development 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 generating embeddings.
  • Applying retriever and re-ranker patterns.
  • Hands-on lab: Creating a RAG pipeline.

Integrations and Enterprise Data

  • Establishing secure connectors to internal systems.
  • Managing data governance and access controls.
  • Hands-on lab: Connecting enterprise data sources.

Testing, Evaluation, and Iteration

  • Conducting prompt testing and applying evaluation metrics.
  • Employing user simulation and validation strategies.
  • Hands-on lab: Evaluating and tuning the agent.

Deployment, Monitoring, and Maintenance

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

Summary and Next Steps

Requirements

  • Foundational understanding of natural language processing.
  • Experience working with cloud services and APIs.
  • Familiarity with search engines and vector databases.

Audience

  • Developers.
  • Solution architects.
  • Product managers.
 14 Hours

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