AI Programming involves the practical application of programming tools and techniques to develop intelligent systems that facilitate automation, analysis, and service improvement.
This instructor-led, live training (available online or onsite) is designed for public sector professionals with limited or developing experience in AI who are involved in innovation, research, or operational transformation. The course aims to equip participants with practical programming skills to explore how AI tools can be constructed and integrated into government workflows.
By the conclusion of this training, participants will be able to:
- Grasp core AI concepts, including large language models, APIs, and intelligent automation.
- Write simple Python scripts to invoke AI services and process structured data.
- Develop prototypes using AI for tasks such as summarization, classification, or chatbot creation.
- Assess the risks and constraints associated with AI development in the public sector, such as privacy, explainability, and compliance.
Format of the Course
- Interactive lecture and discussion.
- Hands-on application of Python and LLM APIs using 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.
Course Outline
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