Generative AI Foundations

Instructor:

Mental Forge

Category:

AI Courses

Last Update:

February 27th, 2026

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Generative AI Foundations

Short Summary

A beginner-friendly course that explains how generative AI and large language models (LLMs) work, using simple language and practical examples. Learn what is happening behind the scenes, what to trust, what to double-check, and how to choose the right approach for real tasks.

About This Course

AI tools are everywhere, but most people only see the chat box. This course helps you understand what is actually happening so you can use these tools with better judgment. You will learn the core ideas behind LLMs, tokens, context windows, training vs usage, why models make mistakes, and how systems like RAG and fine-tuning differ.

The goal is not to turn you into a researcher. The goal is to help you make smarter decisions: which tool to use, how to reduce errors, how to protect data, and how to explain AI outputs inside a team or organisation.

Topics of Course

Module 1 — What Generative AI Really Is
What “generative AI” means (and what it does not mean) What an LLM is: a plain-language explanation Why these tools feel smart (and where that feeling breaks)

Module 2 — Tokens, Context, and How Models “Read”
Tokens: the model’s unit of text Context window: what the model can “see” at one time Why long conversations drift Practical tip: how to structure inputs for better answers

Module 3 — Why LLMs Make Mistakes
Hallucinations: why they happen When the model guesses instead of knows The difference between “sounds correct” and “is correct” Simple verification habits you can use every time

Module 4 — Knowledge, Sources, and Citations
Training data vs live data Why some answers cannot be sourced Asking for references the right way Building a short source-check routine

Module 5 — Embeddings and Search
What embeddings are (without math overload) Why embeddings help with search and recommendations When embedding-based search beats a normal keyword search Real use cases: FAQs, internal docs, knowledge bases

Module 6 — RAG vs Fine-Tuning vs Prompting
Prompting: when it is enough RAG (retrieval-augmented generation): what it changes Fine-tuning: what it changes (and what it does not) Which approach fits which problem (decision guide)

Module 7 — Safety, Privacy, and Responsible Use
What not to paste into an AI tool Handling sensitive data and private documents Risk areas: legal, health, finance, confidential work Building a basic “safe use policy” for yourself or a team

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Generative AI Foundations

Generative AI Foundations