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Course Outline

Core Principles of AI Coding

  • Defining AI coding: essential concepts and real-world examples
  • AI applications in the public sector: chatbots, summarization tools, and intelligent search
  • Distinguishing AI models from conventional programming logic

Foundational Python for AI

  • Creating initial Python scripts
  • Navigating data structures and control flow mechanisms
  • Utilizing key libraries for AI development: requests, pandas, and json

Integrating AI APIs

  • Understanding APIs: secure access to AI models
  • Transmitting text and structured data to models
  • Interacting with platforms such as OpenAI, Cohere, or Hugging Face

Developing Basic AI Utilities

  • Constructing a document summarization tool
  • Prototyping a chatbot for citizen-facing services
  • Applying AI for the automatic labeling of public datasets

Assessing Outputs and Constraints

  • Comprehending the probabilistic nature of AI behavior
  • Techniques in prompt engineering and controlling output quality
  • Conducting red-teaming exercises to identify bias and hallucinations

Regulatory, Ethical, and Responsible Practices

  • Addressing privacy and explainability mandates in government contexts
  • Comparing open-source and proprietary models: advantages and limitations
  • Establishing a checklist for safe experimentation and scaling

Recap and Future Directions

Requirements

  • Fundamental proficiency in handling spreadsheets or structured data
  • Knowledge of public sector service delivery or analytical tasks
  • No previous programming background is necessary, as introductory Python concepts will be provided

Target Audience

  • Civil servants and analysts looking to incorporate AI into their daily operations
  • Digital government specialists aiming to gain hands-on capabilities in AI integration
  • Government teams focused on innovation, transformation, and research
 14 Hours

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