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Keep shoppingGenerative AI and Large Language Models for Beginners
2 students
Generative AI and Large Language Models for Beginners
Understand tokens, transformers, LLM, Gen AI, hallucination, RAG and agents.
Created byPianalytix • 97,000+ Students Worldwide
Last updated 8/2026
English
What you'll learn
- Explain what actually produces an A.I. answer: predicting one small piece of text at a time, nothing else
- Read a token count, and explain why tokens, not words, are the unit you are billed in
- Describe what pre-training stores, and why a model cannot quote the sources it learned from
- Choose correctly between fine-tuning and retrieval when a model needs your organisation's facts
- Explain why a context window fills up, and why a bigger window is not automatically better
- Name the two real causes of invented answers, and the checks that catch them before anyone else sees them
- Decide when a reasoning model is worth the extra time and cost, and when a fast answer is the better buy
- Write a prompt that carries the task, the context, the format, and permission for the model to say it does not know
- Explain what happens to your own documents in a RAG system before the model ever sees them
- Apply a clear rule for what must never be pasted into an A.I. tool, and stop-rules for when not to use it at all
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Course content
3 sections • 17 lectures • 52m total length
Requirements
- You have used a chat-based A.I. assistant at least once. Anything counts, even one question.
Description
You asked an A.I. tool a question. It gave back an answer that was fluent, confident, neatly organised, and impossible for you to check.
This is a beginner course on how generative A.I. and large language models actually produce that answer, explained in plain English with no code anywhere in it. It sits in the space between the short videos that stop at definitions and the longer courses that ask you to open a Python notebook three modules in.
One request, followed all the way through
A single realistic task runs across all 16 lessons: a professional asks an assistant to summarise a supplier contract. Each lesson shows one more part of what happens to that request, and one more thing that can go wrong with it. Tokens. Attention. The context window that fills up. The clause the model invented that was never in the document.
By the end you will be able to say, in your own words, why the model forgot the top of your contract, why it invented that clause, why fine-tuning would not have fixed it, and what you should have done instead.
What makes it different
It is checked against primary sources. Four claims repeated constantly in beginner material are contradicted by the papers they rest on. You get the accurate version and a plain statement that the popular version is wrong.
A full lesson on what you must never paste into an A.I. tool. That is the question your employer asks long before anyone asks how a transformer works.
It covers reasoning models, so it matches what you see on screen when a model pauses to think before it answers.
It ends on stop-rules, not encouragement. The last lesson is about the tasks where generative A.I. is the wrong choice no matter how good your prompt is.
The code-free promise, stated in lesson one
No programming. No mathematics. No API key. No account to open and nothing to install. If you have used a chat assistant once and can follow a diagram, you have everything you need to start.
What this course does not cover
It does not build anything. There is no writing code against a model API, no training or hosting a model, no tour of fifty A.I. tools, no prompt library for one specific product, and no legal advice about A.I. use in your jurisdiction. If your goal is to ship an A.I. application, this is the wrong course, and the closing lesson names what to go to instead.
Who this course is for:
- Working professionals who are already using A.I. tools at work and want to understand what is happening underneath the answer.
- Managers, analysts, consultants and operations people who have to make decisions about A.I. without being the ones who build it.
- Career changers and students who want an accurate mental model of the technology before committing to a longer technical course.