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Course Outline

Introduction to Agent Builder and RAG

  • Overview of Agent Builder capabilities
  • RAG fundamentals and application scenarios
  • Use cases and success stories

Environment Setup

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

Designing Grounded Agent Workflows

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

Implementing RAG Pipelines

  • Indexing documents and managing embeddings
  • Utilizing retriever and re-ranker patterns
  • Hands-on lab: Building a RAG pipeline

Integrations and Enterprise Data

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

Testing, Evaluation, and Iteration

  • Prompt testing and evaluation metrics
  • Strategies for user simulation and validation
  • 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 understanding of natural language processing
  • Experience with cloud services and APIs
  • Knowledge of search and vector databases

Target Audience

  • Developers
  • Solution architects
  • Product managers
 14 Hours

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