Summer Training Program 2026

What to Expect:

A welcoming, no-pressure introduction to the program — no technical content yet, so every student starts on equal footing.

Agenda & Focus Areas:

• Meet Ejo Labs Team: Welcome and team introductions to instructors, program leads, and peers.
• Program Overview: Program goals, expectations, code of conduct, and walkthrough of the 5-week journey and capstone.
• Interactive Icebreakers & Quiz: Icebreaker games and a multiple-choice tech & AI quiz.
• Open Q&A: Answering questions and ensuring all students are ready for Session 1.

Learning Objectives:

• Understand what programming is, what engineers do, and explore careers in technology.
• Explain artificial intelligence in simple terms and identify real-world AI examples.
• Apply computational thinking: breaking problems into smaller parts, finding patterns, and writing simple algorithms.

Topics Covered:

• Welcome to Technology & AI: programming, engineering roles, real-world AI, and tech careers.
• Computational thinking: problem decomposition, pattern recognition, and algorithmic thinking.
• How AI is changing the world with practical real-life demonstrations.

Learning Objectives:

• Install Python, set up VS Code, and run your first program.
• Understand variables, data types (numbers, text), and perform math operations.
• Capture user input and print formatted output back to the screen.

Topics Covered:

• Development setup: Installing Python, using VS Code, running scripts.
• Python syntax: Variables, data types (numbers, text), getting user input, and printing output.

Learning Objectives:

• Understand what an API is and why applications use APIs to communicate.
• Make a simple API request using Python and read returned data.
• Use APIs to bring real-world information into a Python program and build a simple project using a public API.

Topics Covered:

• API fundamentals: Application communication, HTTP requests, reading data payloads.
• Python API integration: Querying public endpoints using Google Colab.

Learning Objectives:

• Understand what n8n is and how workflow automation works.
• Navigate the n8n interface, create a basic workflow, and connect different services.
• Work with triggers, actions, data, and workflow logic to build and test a simple real-world automation.

Topics Covered:

• Workflow automation: n8n interface, triggers, actions, and data logic.
• Connecting services and automating repetitive tasks for AI-powered workflows.

Learning Objectives:

• Write clear, effective prompts (instructions, context, examples, role prompting).
• Understand Large Language Models, tokens, context windows, temperature, and hallucinations.
• Debug prompts, manage context, and compare good vs. bad prompts.

Topics Covered:

• Prompt engineering: prompt structure, role prompting, few-shot prompting, prompt versioning, AI coding assistants.
• LLM fundamentals: tokens, context windows, temperature, cost vs. quality, and model selection.

Learning Objectives:

• Connect a simple application to an AI model (sending prompts & receiving responses).
• Build a basic chatbot interface and design a useful AI tool.
• Study live AI products and demonstrations (Safiya AI).

Topics Covered:

• API model connections: prompt payloads, model outputs, chat interface design.
• Case study & demonstration: showcasing Safiya AI and building simple AI assistants.

Learning Objectives:

• Call LLM APIs from code, get structured outputs, and stream AI responses to UI.
• Understand text embeddings, document chunking, and Retrieval-Augmented Generation (RAG).
• Work with vector databases for document ingestion and context retrieval.

Topics Covered:

• LLM API integration: structured output parsing, response streaming, and error handling.
• Embeddings & RAG architecture: document chunking, vector database ingestion, and semantic search.

Learning Objectives:

• Identify AI use-case opportunities and write a clear problem description.
• Perform a SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) of a proposed solution.
• Identify user personas and apply design thinking principles.

Topics Covered:

• Opportunity discovery: identifying AI use cases and writing problem descriptions.
• Solution design: SWOT analysis, user personas, and design thinking.

Learning Objectives:

• Understand AI Security: prompt injection, protecting API keys, and managing secrets safely.
• Apply Responsible AI principles: user privacy, ethics, monitoring, and incident response basics.
• Receive mentorship on tech career habits and project guidance.

Topics Covered:

• AI Security & Responsible AI: prompt injection defense, API key security, secret management, user privacy.
• Principles of responsible AI, monitoring, incident response, and career inspiration.

Learning Objectives:

• Understand why African languages matter in the AI era & build community-driven language datasets.
• Explore multilingual LLMs, designing trustworthy AI products, responsible AI, and language inclusion.
• Participate in a hands-on case study designing an AI solution for a local African language challenge.

Topics Covered:

• Language resources, dataset creation, multilingual LLMs, and language technology applications.
• Responsible AI, language rights, trustworthy AI design, and future opportunities in African AI innovation.

Learning Objectives:

• Explain what Machine Learning is and how it differs from traditional programming; identify ML problem types.
• Understand the end-to-end ML workflow: dataset collection, data preparation, model training, testing, evaluation, and parameter tuning.
• Recognize and handle Machine Learning challenges such as overfitting and underfitting.

Topics Covered:

• ML core concepts, problem classification, dataset collection, and data preprocessing pipelines.
• Classical ML algorithms, model evaluation metrics, hyperparameter tuning, and overfitting vs. underfitting.

Learning Objectives:

• Adopt an engineering mindset and understand professionalism in technology.
• Build an outstanding GitHub portfolio, write a technical CV, and optimize your LinkedIn profile.
• Prepare for open source, interviews, engineering communication, and project presentations.

Topics Covered:

• Professional skills: GitHub portfolio structure, technical CVs, LinkedIn optimization.
• Contributing to open source, interview preparation, engineering communication, and Demo Day pitch prep.