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Course Outline

Introduction to Agent Builder and RAG

  • Overview of Agent Builder capabilities.
  • RAG fundamentals and when to apply them.
  • Use cases and success stories.

Setting Up the Environment

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

Designing Grounded Agent Workflows

  • Defining agent goals and conversation flows.
  • Mapping data sources to retrieval strategies.
  • Hands-on lab: building a conversation flow.

Implementing RAG Pipelines

  • Indexing documents and embeddings.
  • Retriever and re-ranker patterns.
  • Hands-on lab: creating a RAG pipeline.

Integrations and Enterprise Data

  • Secure connectors to 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 drift.
  • Operational playbooks for updates and rollback.

Summary and Next Steps

Requirements

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

Audience

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

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