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Course Outline

Introduction to Agent Builder and RAG

  • Survey of Agent Builder’s capabilities.
  • Core principles of RAG and appropriate use cases.
  • Real-world applications and success narratives.

Environment Setup

  • Configuring the Vertex AI workspace.
  • Linking search tools and vector stores.
  • Practical lab: preparing the environment.

Designing Grounded Agent Workflows

  • Establishing agent objectives and dialogue flows.
  • Aligning data sources with retrieval strategies.
  • Practical lab: constructing a dialogue flow.

Implementing RAG Pipelines

  • Indexing documents and generating embeddings.
  • Utilizing retriever and re-ranking patterns.
  • Practical lab: building a RAG pipeline.

Integrations and Enterprise Data

  • Secure connections to internal systems.
  • Data governance and access control measures.
  • Practical lab: linking enterprise data sources.

Testing, Evaluation, and Iteration

  • Prompt testing and evaluation metrics.
  • User simulation and validation strategies.
  • Practical lab: assessing and fine-tuning the agent.

Deployment, Monitoring, and Maintenance

  • Deployment strategies and scaling considerations.
  • Tracking performance, relevance, and drift.
  • Operational guidelines for updates and rollbacks.

Summary and Next Steps

Requirements

  • Fundamental understanding of natural language processing.
  • Practical experience with cloud services and APIs.
  • Knowledge of search mechanisms and vector databases.

Target Audience

  • Software developers.
  • Solution architects.
  • Product managers.
 14 Hours

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