Practical AI Skills Training in Nepal

Course Overview

This is AITC’s 5-day practical AI skills course in Nepal: 10 hours of hands-on training for students, teachers, working professionals, and beginners who can already ask a chatbot a question and want to do far more with it. You’ll leave able to write prompts that actually get you useful answers, check an AI-generated answer against its source before you trust it, use AI for real research and writing tasks, put together a small image, video, and audio campaign, and build one working no-code automation.

What Is Practical AI Skills Training?

“Practical AI Skills” refers to the working knowledge needed to use AI tools like ChatGPT, Claude and Gemini reliably and responsibly, not just how to type a question, but how to structure a prompt, check the answer, and apply the same skills to research, writing, media and automation.

The course is built around five connected skill areas:

• Prompting and context — structuring a prompt around task, context, constraints and output format, and organizing reusable instructions so you’re not starting from scratch every time.
• Verification — checking an AI-generated answer against its actual source and recognizing when a chatbot is likely wrong, rather than assuming it’s right because it sounds confident.
• Research and writing — using AI to summarize documents, extract evidence, and draft or edit real writing, with the same verification habit applied throughout.
• Media production — planning and assembling a small image, video and audio campaign using AI generation tools alongside a tool like Canva.
• No-code automation — connecting a trigger, an AI step and an output into one working workflow, and understanding what data that workflow can actually access.

Because these five areas build on each other — prompting feeds research, research feeds writing, and verification runs through all of it — the course treats “using AI well” as one connected skill rather than five separate tricks, which is what makes it different from a single tool tutorial.

What you’ll be able to do by the end

• Explain AI, machine learning, deep learning, generative AI, and large language models in plain, everyday language.
• Structure prompts with clear task, context, constraints, and output format — and improve them through iteration rather than guessing.
• Check an AI-generated claim against its actual source, and recognize when a chatbot is hallucinating.
• Use AI to work with real documents: summarizing, extracting evidence, comparing information and drafting.
• Put together a small, cohesive media package — image, short video or storyboard, and narration or slides.
• Tell the difference between a fixed automation and a goal-directed AI agent, and know what data each can access.
• Build and test one working no-code automation from a real trigger to a real output.
• Apply privacy, copyright, bias, and disclosure basics to each of the above, not just as a separate lecture topic.

Tools you’ll use in class

• ChatGPT, Claude, Gemini — Day 1 (“through a short comparison in ChatGPT, Claude or Gemini”)
• Claude Projects / Gemini Gems / Custom GPTs — Day 2 (“Organize reusable instructions and files in a Claude Project, Gemini Gem or Custom GPT if available”)
• NotebookLM — Day 3 (“Try NotebookLM or document upload”)
• Canva — Day 4 (“assemble in Canva”)
• Flow or Veo — Day 4 (“create or demonstrate a short video with Flow or Veo”)
• n8n, Make, Zapier — Day 5 (“Build a no code workflow in n8n, Make or Zapier”)

Who is this course for?

Students preparing for coursework and job applications, teachers who want to use AI responsibly in the classroom, working professionals who write reports, emails, or content, and complete beginners who’ve used ChatGPT, Claude, or Gemini a handful of times but don’t yet trust their own prompts.

If you’re looking for a coding-heavy AI/ML or data science programme, this isn’t it — see our Data Science with Python training instead.

Capstone Standard

Every learner’s final project — whether it’s the research workflow, the media campaign, or the automation — is presented against the same six checkpoints:

• Problem: what you were actually trying to solve?
• Input: what went into the AI step?
• AI step: what the AI did?
• Result: what came out?
• Verification: how you checked it was right?
• Privacy: what data was involved and who could see it?

This is the standard used to grade the capstone, and it’s also the habit the whole course is built to install: don’t just take an AI’s output — check it, and know what it touched.

Certification

You will receive a Certificate of Completion after successfully finishing the course and completing all required projects.