Advanced Technology
Artificial Intelligence (AI)
Understand how AI systems work and how they are applied in real-world products.
Prerequisites
No prior experience required — open to beginners.
Certificate
Awarded by Rescue Academy on successful completion of the program's assessments and final project.
Learn Online — Live Classes
Register, receive your schedule, and join live instructor-led Artificial Intelligence (AI) classes on Zoom. Assignments, instructor feedback, and a certificate on completion.
Learn In Person
Attend Artificial Intelligence (AI) classes in person in Juba with hands-on labs, instructor mentorship, and a certificate on completion.
What You'll Learn
- What AI is, how it learns and why it makes mistakes
- Neural networks and generative AI explained
- Using AI tools effectively and checking their outputs
- Calling AI model APIs from Python and prompt engineering
- Retrieval-augmented assistants and AI evaluation
- AI system design, agents and tool use, open models
- AI safety, security, governance and responsible adoption
Program Objectives
- Explain how AI works and judge where it is useful and where it is not
- Use AI tools effectively while verifying outputs and protecting data
- Build grounded, evaluated AI assistants with Python and model APIs
- Design, secure and test AI systems, including agents, for real conditions
- Govern AI responsibly with policies, assessments and value measurement
Who It's For
- Professionals and students who want to understand and use AI confidently
- Developers who want to build AI-powered applications
- Managers responsible for AI adoption and policy in organisations
Where You'll Use It
- Public services, NGOs and businesses adopting AI assistants
- Document-heavy work such as case management, reporting and customer support
- AI policy, governance and procurement roles
Curriculum
Instructor-led, practical training. Each level is a block of 4, 8 or 12 weeks; every module ends with a practical activity and an assessment, and every level with a project.
Module 1: Understanding Artificial Intelligence 10 h
Learn what artificial intelligence is, how it developed, the main kinds of AI and where it already appears in daily life. 3 lessons
Module 2: How AI Learns from Data 11 h
Understand how AI systems learn patterns from examples, how trained models are used and why they make mistakes. 3 lessons
Module 3: Neural Networks and Generative AI Explained 10 h
Understand, without heavy mathematics, how neural networks work, how deep learning recognises images and speech, and how generative AI creates text and images. 3 lessons
Module 4: Using AI Tools Effectively 11 h
Use AI assistants and tools for real tasks with clear prompts, careful checking and appropriate trust. 3 lessons
Module 5: AI Ethics and Responsible Use 10 h
Understand bias, privacy, misinformation and accountability in AI, and apply responsible practices at work. 3 lessons
Level project: AI Use Guide for a Local Organisation
Module 1: Python for AI Applications 15 h
Use the Python needed for AI applications: working with JSON and HTTP APIs, managing secrets and handling errors. 3 lessons
Module 2: Working with AI Model APIs 16 h
Call large language model APIs from Python, control their behaviour, get structured outputs and manage cost. 3 lessons
Module 3: Prompt Engineering 15 h
Design prompts systematically, use examples and structure, and test prompts like software. 3 lessons
Module 4: Retrieval-Augmented Generation 16 h
Ground AI answers in an organisation's own documents using embeddings, search and cited generation. 3 lessons
Module 5: Evaluating AI Outputs 13 h
Evaluate AI systems systematically with test sets, rubrics, automated checks and human review. 3 lessons
Module 6: Intermediate AI Project 15 h
Deliver the grounded FAQ assistant as a working, evaluated prototype with safeguards and a clear report. 3 lessons
Level project: Grounded FAQ Assistant for a Public Service
Module 1: Designing AI Systems 22 h
Decide where AI adds value, design system architecture with humans in the loop and choose models for real-world constraints. 4 lessons
Module 2: AI Agents and Tool Use 22 h
Build systems where models call tools and take multi-step actions, with permissions, guardrails and evaluation. 4 lessons
Module 3: Open Models and Customisation 22 h
Run open models locally and choose between prompting, retrieval and fine-tuning to adapt AI to an organisation's needs and languages. 4 lessons
Module 4: AI Safety, Security and Testing 22 h
Protect AI systems from prompt injection, data leaks and misuse, test them adversarially and monitor them in production. 4 lessons
Module 5: AI Governance and Strategy 22 h
Govern AI in organisations with policies, risk frameworks, impact assessments and measurement of value. 4 lessons
Module 6: AI Capstone Project 30 h
Design, build, test and govern an AI assistant system for a realistic organisational need, and recommend whether to adopt it. 5 lessons
Level project: AI Assistant System with Governance Pack
Final practical project
AI Assistant System with Governance Pack
A working AI system with retrieval and tools for a realistic organisational need, tested and red-teamed, with the policy, impact assessment and monitoring plan needed to adopt it, and a go/no-go recommendation.
Deliverables
- A working system with retrieval, a tool, permissions and logging
- An evaluation and red-team report with fixes
- A governance pack: use policy, impact assessment and monitoring plan
- A presentation with an adoption recommendation
Assessed on
- The design fits the users, constraints and risks
- Sensitive actions require human approval
- Testing covers accuracy, injection and misuse, with fixes shown
- Governance documents are consistent with the system as built
Worked Examples
A selection of the visual, practical examples used in this program's lessons — each teaches a different skill.
- Step-by-step Checking an AI assistant's answer before you use it A routine for turning a fluent AI answer into information you can trust — or deciding not to use it. Beginner · Module 3 · Lesson 3
- Code example Semantic search in a few lines: cosine similarity Rank documents by how close their meaning is to a question, even when they use different words. Intermediate · Module 4 · Lesson 1
- Workflow How a retrieval-augmented assistant answers a question From an organisation's documents to a cited answer — and an honest 'I don't know' when the documents are silent. Intermediate · Module 4 · Lesson 2
- Dashboard Evaluating a school-registration FAQ assistant Results of a 40-question evaluation by question type, with what to fix next. Intermediate · Module 5 · Lesson 3
- Troubleshooting tree Should we use AI for this task? A quick check before building or buying an AI solution for an organisational task. Advanced · Module 1 · Lesson 1
- Case study A hidden instruction in an uploaded document A composite, illustrative case of indirect prompt injection in a document assistant, and the layered fixes that followed. Advanced · Module 4 · Lesson 1
Free Course: Artificial Intelligence Starter
A free introduction to what AI is and is not: how systems learn from data, what neural networks do, real tools and applications, and how to use AI responsibly. Free, self-study, about 5 hours — no certificate. The full program above is taught live by an instructor, with practical assignments, assessments and a certificate.
- 1Free lesson 1What is AI? Concepts & History
- 2Free lesson 2How AI Learns from Data
- 3Free lesson 3Neural Networks & Deep Learning
- 4Free lesson 4AI Tools & Real-World Applications
- 5Free lesson 5AI Ethics, Bias & Responsible Use
- Final checkQuick Quiz — Artificial Intelligence Basics
Tools & Technologies
Primary Tools
- AI chat assistants — Hands-on use and evaluation of generative AI at Beginner level Practice: Prompting and verifying outputs
- Python — Building AI applications from the Intermediate level Practice: Scripts that call APIs and process data
- AI model APIs (hosted language and embedding models) — Building assistants and pipelines Practice: Structured outputs, retrieval and cost control
Supporting Tools
- No-code model training tools — Seeing training and testing in action without code Practice: Understanding how AI learns
- Embedding models and a vector index — Semantic search for retrieval-augmented generation Practice: Grounding answers in documents
- Git — Versioning prompts, code and evaluation sets Practice: Reproducible AI development
Professional / Industry Tools
- Open-weight models run locally — Private and offline AI at Advanced level Practice: Evaluating and deploying open models
- NIST AI RMF and UNESCO AI ethics recommendation — Frameworks for responsible AI governance Practice: Impact assessments and AI policies
Career Opportunities
- Data/AI analyst role at a bank, telecom, or NGO
- Research or further-study pathway in AI/ML/Data Science
- Remote/freelance AI or data work
Practical Projects
- Use an AI tool (e.g. ChatGPT/Gemini) to solve a real productivity or business problem, documenting the process
- Research and present a real-world AI application relevant to South Sudan
- Given a real business idea, decide whether AI is actually the right tool to solve it or whether a simpler non-AI approach would work better — document your reasoning either way
Problem-Solving Focus
Problem: A business or NGO wants to "add AI" to look modern, without a clearly defined problem it's actually meant to solve
Approach: Work backward from a real, well-defined problem and success metric before choosing any tool — and be willing to conclude that a simpler rule-based approach solves it just as well, without AI at all.
Problem: An AI chatbot or writing assistant gives a confident-sounding answer that turns out to be wrong or misleading
Approach: Treat AI output as a draft to verify, not a finished answer — cross-check facts independently, especially for anything with real consequences like health, legal, or financial decisions.
Problem: An AI tool trained mostly on data from other parts of the world gives advice or examples that don't fit the local South Sudanese context — currency, language, or cultural norms
Approach: Adapt AI output deliberately for local context instead of using it unedited, and learn to recognize when a tool's blind spots come directly from what it was trained on.
Problem: A team wants to use AI to help with hiring, lending, or resource-allocation decisions that affect real people
Approach: Evaluate the tool for bias and unfair outcomes across different groups before deploying it, and keep a human able to review and override the AI's decisions rather than automating the decision away entirely.
Ready to register for Artificial Intelligence (AI)?
Register — Learn Online Register — Learn In Person WhatsApp: +211926196668 Email: rescueacademy26@gmail.com