Introduction to AI Programming for Public Sector Innovation

• Basic experience working with spreadsheets or structured data
• Familiarity with public sector service delivery or analysis tasks
• No prior programming experience required (introductory Python will be covered)

Audience

• Public servants and analysts exploring AI use in daily workflows
• Digital government professionals seeking hands-on skills in AI integration
• Innovation, transformation, and research teams in government

AI Programming is the practical application of programming tools and techniques to create intelligent systems that assist in automation, analysis, and service enhancement.

This instructor-led, live training (online or onsite) is aimed at public sector professionals with limited or emerging experience in AI who support innovation, research, or operational transformation and wish to gain practical programming skills to explore how AI tools can be built and applied within government workflows.

By the end of this training, participants will be able to:

• Understand core AI concepts, including large language models, APIs, and intelligent automation.
• Write simple Python scripts to call AI services and process structured data.
• Create prototypes using AI for tasks like summarization, classification, or chatbot development.
• Evaluate risks and constraints of AI development in the public sector (privacy, explainability, compliance).

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of Python and LLM APIs in public sector examples.
• Guided exercises focused on data analysis, content automation, and workflow prototyping.

Course Customization Options

• To request a customized training for this course based on your department's workflows or internal tools, please contact us to arrange.

Foundations of AI Programming

• What is AI programming? Key concepts and examples
• Public sector applications of AI: chatbots, summarizers, intelligent search
• AI models vs traditional programming logic

Introductory Python for AI

• Writing your first Python scripts
• Working with data structures and control logic
• Libraries for AI programming: requests, pandas, json

Using AI APIs

• What is an API? Accessing AI models securely
• Sending text and structured data to models
• Working with OpenAI, Cohere, or Hugging Face APIs

Creating Simple AI Tools

• Building a document summarizer
• Prototyping a chatbot for citizen services
• Using AI to auto-label public datasets

Evaluating Outputs and Limitations

• Understanding probabilistic AI behavior
• Prompt engineering and managing output quality
• Red-teaming your prototypes for bias and hallucinations

Compliance, Ethics, and Responsible Development

• Privacy and explainability requirements in government
• Open-source vs proprietary models: pros and cons
• Checklist for safe experimentation and scale-up

Summary and Next Steps