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Course Outline

Introduction to Agent Builder and RAG

  • Comprehensive overview of Agent Builder capabilities.
  • Fundamentals of RAG and applicable use scenarios.
  • Real-world use cases and success stories.

Environment Setup

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

Designing Grounded Agent Workflows

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

Implementing RAG Pipelines

  • Document indexing and embedding processes.
  • Patterns for retrievers and re-rankers.
  • Hands-on lab: Building a RAG pipeline.

Integrations and Enterprise Data

  • Secure connectors for internal systems.
  • Data governance protocols and access controls.
  • Hands-on lab: Linking enterprise data sources.

Testing, Evaluation, and Iteration

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

Deployment, Monitoring, and Maintenance

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

Summary and Next Steps

Requirements

  • Fundamental understanding of natural language processing (NLP).
  • Practical experience with cloud services and APIs.
  • Working knowledge of search engines and vector databases.

Target Audience

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

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Price per participant

Provisional Upcoming Courses (Require 5+ participants)

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