Course Outline

Introduction to LlamaIndex for Government

  • Understanding LlamaIndex and its Role in Language Models (LLMs)
  • Setting Up LlamaIndex: Environment and Prerequisites for Government Use
  • The Basics of Indexing Custom Data for Government Applications

LlamaIndex in Action for Government

  • Querying with LlamaIndex: Techniques and Best Practices for Government Operations
  • Building Query and Chat Engines with LlamaIndex for Enhanced Government Services
  • Creating Intuitive Streamlit Interfaces for LLM Applications in the Public Sector

Advanced LlamaIndex Features for Government

  • Employing Retrieval-Augmented Generation (RAG) for Enhanced Data Retrieval in Government Operations
  • Leveraging Vectorstores for Efficient Data Management in Government Systems
  • Designing and Implementing LlamaIndex Agents for Government Applications

Application Development with LlamaIndex for Government

  • Prompt Engineering: Chain of Thought, ReAct, Few-Shot Prompting for Government Use Cases
  • Developing a Documentation Helper: A Real-World LLM Application for Government
  • Debugging and Testing LLM Applications in the Public Sector

Deployment and Scaling for Government

  • Deploying LlamaIndex-Based Applications for Government Use
  • Scaling LLM Applications for High Performance in Government Operations
  • Monitoring and Optimizing LLM Applications for Government Efficiency

Ethical and Practical Considerations for Government

  • Navigating Ethical Implications in LLM Applications for Government
  • Ensuring Privacy and Data Security with LlamaIndex for Government Operations
  • Preparing for Future Developments in LLM Technology for Government Use

Summary and Next Steps for Government

Requirements

  • An understanding of Python programming and foundational machine learning concepts.
  • Experience with APIs and application development for government systems.
  • Familiarity with natural language processing is beneficial but not required.

Audience

  • Developers
  • Data Scientists
 42 Hours

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