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Course Outline

Introduction to Agent Builder and RAG

  • Overview of Agent Builder capabilities.
  • RAG fundamentals and appropriate use cases.
  • Real-world use cases and success stories.

Environment Setup

  • Configuring the Vertex AI workspace.
  • Establishing connections to 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

  • Document indexing and embedding creation.
  • Implementing retriever and re-ranker patterns.
  • Hands-on lab: Building a RAG pipeline.

Integrations and Enterprise Data

  • Secure connectivity to internal systems.
  • Data governance and access control mechanisms.
  • Hands-on lab: Connecting to enterprise data sources.

Testing, Evaluation, and Iteration

  • Prompt testing and evaluation metrics.
  • Strategies for user simulation and validation.
  • Hands-on lab: Evaluating and tuning agent performance.

Deployment, Monitoring, and Maintenance

  • Deployment options and scaling considerations.
  • Monitoring performance, relevance, and drift.
  • Operational playbooks for updates and rollback procedures.

Summary and Next Steps

Requirements

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

Audience

  • Software Developers
  • Solution Architects
  • Product Managers
 14 Hours

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