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Course Outline

Introduction to Agent Builder and RAG

  • Review of Agent Builder functionalities.
  • RAG fundamentals and application scenarios.
  • Use cases and success stories.

Environment Setup

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

Designing Grounded Agent Workflows

  • Establishing 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: Developing a RAG pipeline.

Integrations and Enterprise Data

  • Secure connectors for internal systems.
  • Data governance and access control management.
  • Hands-on lab: Connecting enterprise data sources.

Testing, Evaluation, and Iteration

  • Prompt testing and evaluation metrics.
  • User simulation and validation techniques.
  • Hands-on lab: Evaluating and tuning agent performance.

Deployment, Monitoring, and Maintenance

  • Deployment strategies and scaling considerations.
  • Monitoring performance, relevance, and drift.
  • Operational protocols 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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