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Course Outline
Introduction to Agent Builder and RAG
- Overview of Agent Builder capabilities
- RAG fundamentals and when to use them
- Use cases and success stories
Setting Up the Environment
- Configuring Vertex AI workspace
- Connecting search and vector stores
- Hands-on lab: environment preparation
Designing Grounded Agent Workflows
- Defining agent goals and conversation flows
- Mapping data sources to retrieval strategies
- Hands-on lab: building a conversation flow
Implementing RAG Pipelines
- Indexing documents and embeddings
- Retriever and re-ranker patterns
- Hands-on lab: creating a RAG pipeline
Integrations and Enterprise Data
- Secure connectors to internal systems
- Data governance and access controls
- Hands-on lab: connecting enterprise data sources
Testing, Evaluation, and Iteration
- Prompt testing and evaluation metrics
- User simulation and validation strategies
- 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
- Basic knowledge of natural language processing
- Experience with cloud services and APIs
- Familiarity with search and vector databases
Audience
- Developers
- Solution architects
- Product managers
14 Hours
회원 평가 (1)
강사 실시간으로 질문에 답변합니다.
Adrian
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