Course Outline

Introduction to LlamaIndex for Government

  • Understanding LlamaIndex and its role in Language Learning 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 workflows
  • Building query and chat engines with LlamaIndex to enhance public sector operations
  • Creating intuitive Streamlit interfaces for LLM applications in the government context

Advanced LlamaIndex Features for Government

  • Employing retrieval-augmented generation (RAG) for enhanced data retrieval in government systems
  • Leveraging vectorstores for efficient data management in public sector applications
  • Designing and implementing LlamaIndex agents tailored to government needs

Application Development with LlamaIndex for Government

  • Prompt engineering: chain of thought, ReAct, few-shot prompting for government-specific tasks
  • Developing a documentation helper: a real-world LLM application for government use
  • Debugging and testing LLM applications to ensure reliability in public sector environments

Deployment and Scaling for Government

  • Deploying LlamaIndex-based applications in government settings
  • Scaling LLM applications for high performance in the public sector
  • Monitoring and optimizing LLM applications to meet government standards

Ethical and Practical Considerations for Government

  • Navigating ethical implications in LLM applications within the government context
  • Ensuring privacy and data security with LlamaIndex for government operations
  • Preparing for future developments in LLM technology to support public sector innovation

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
  • Familiarity with natural language processing is beneficial but not required

Audience

  • Developers
  • Data Scientists
 42 Hours

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