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Course Outline

Introduction to Agent Builder and RAG

  • Overview of Agent Builder functionalities.
  • Core concepts of RAG and application scenarios.
  • Practical use cases and success stories.

Environment Setup

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

Designing Grounded Agent Workflows

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

Implementing RAG Pipelines

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

Integrations and Enterprise Data

  • Secure connectors for internal systems.
  • Data governance and access control mechanisms.
  • Hands-on lab: Connecting 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 strategies and scaling considerations.
  • Monitoring performance, relevance, and data 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 engines and vector databases.

Target Audience

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

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