Difference between revisions of "Basics-of-Artificial-Intelligence/C2/Generative-AI/English"
(Created page with "'''Spoken Tutorial-AI-3''' '''Generative AI''' '''Meta Tags: '''Generative AI, Predictive AI, Transformer, LLM, Prompt, Model, Training, Inference, Spoken Tutorial, Video Tu...") |
(No difference)
|
Latest revision as of 21:52, 24 November 2025
Spoken Tutorial-AI-3
Generative AI
Meta Tags: Generative AI, Predictive AI, Transformer, LLM, Prompt, Model, Training, Inference, Spoken Tutorial, Video Tutorial, EduPyramids.
Pre-requisite Tutorial: Introduction to Machine Learning and Deep Learning.
| Visual Cue | Narration |
| Title Slide: “Introduction to Generative AI” | Welcome to this Spoken Tutorial on Introduction to Generative AI. |
| Slide 2
Learning Objective Bulleted list |
In this tutorial, you will learn about,
|
| Slide 3
System Requirements Graphic Laptop, browser, internet icons |
To practice this tutorial, you will need:
No coding knowledge is needed. |
| In a previous tutorial, we saw how AI helps computers to think and act smart.
But have you ever wondered how AI makes all this happen? | |
| Slide 4
Generative AI |
Generative AI can do creative tasks that we thought only humans could do. |
| Text highlight: “Create new things” | Generative AI, or GenAI, doesn’t just analyze data but can also create new things. |
| Montage: AI-generated poem, painting, code snippet | It can generate original content such as poems, images, music, code, and more. |
| Split screen: Spam filter vs AI painting | Most AI we use daily is Predictive AI.
Predictive AI classifies data and makes predictions. |
| Text on screen: “Predictive AI → What is this?” | Predictive AI helps in understanding two things.
It can give answers to “What is this?” and “What may happen next?” Predictive AI recognizes things and makes smart guesses about the future. For example, use of a spam filter in an email inbox. |
| Spam filter animation | Here, Predictive AI checks each email and decides whether it is spam or not. |
| Recommender system visual | We use Predictive AI in many every day apps.
Some of them are weather apps, movie apps, map apps, and photo apps. |
| Transition: Text “Generative AI → Create something new” | On the other hand, Generative AI or GenAI, can create something new.
It creates them based on the knowledge it has learned earlier. |
| Astronaut riding horse (AI art) | It can generate a unique image, like an astronaut riding a horse. |
| Poem generation example
Code snippet generation |
It can also write a short poem or generate a working code. |
| Text highlight: “Predictive → classify / Generative → create” | So remember this difference.
Predictive AI classifies and predicts data, whereas GenAI creates examples. |
| Timeline animation → 2017 marker | GenAI uses a model called Transformer.
Transformers process language in parallel, rather than word by word. This makes them faster, smarter, and more efficient. |
| Sentence visualization using a story analogy | Transformers understand how words relate to each other, like we follow a story. |
| Side-by-side: before vs after Transformer | This lets them generate well-structured, meaningful, human-like text.
GenAI grew rapidly after Transformer architecture made a breakthrough in 2017. |
| Transition slide: “Core Terminology” | Now, let’s learn a few important GenAI terms. |
| Brain icon | First, let's understand the term Model.
Think of the Model as the “brain” of the AI system.” A Model can be small and task-specific. It can also be large and for general purposes. For example, ChatGPT is a model trained on large amounts of text. |
| Training animation | Next term is training.
It means teaching the model to use a large amount of data. The model is given numerous examples, including entire books and sample texts. The model learns patterns from this data during training. |
| Prompt box + user typing | Next term is Prompt.
A Prompt is the instruction you give a GenAI model. It tells the model what task it has to perform. For example, “Write a story about a robot.” |
| Text cum Visual
It can be: a full word → “Monday” part of a word → “inter”, “esting” a punctuation mark → “!” a space sometimes even an emoji → 😀 Think of tokens like puzzle pieces.:-AI breaks your text into small pieces (tokens), understands each piece, and then puts the pieces together to form a meaningful response. |
Next term is Tokens.
AI models break the input into small pieces called tokens.* A token is a unit of text. It can be a full word like “Monday” or a part of a word like “inter”, “esting” It can be special characters such as a punctuation mark, or a space or even an emoji. The model reads, understands, and generates text token by token. Think of tokens like puzzle pieces. AI breaks your text into small pieces. Then it understands each piece and then puts the pieces together to form a meaningful response. |
| LLM logo examples (GPT, Gemini, LLaMA) | Next term is LLM.
It stands for Large Language Model. LLM is a powerful model trained on billions of text tokens. For Example: GPT, Gemini, LLaMA. |
| Model responding to prompt | Next term is Inference.
Inference is when the model takes a prompt and generates an output. It’s the stage where the model produces a response after it has been trained. So, when you give a prompt, the model generates text as an output. |
| Summary Slide
Bulleted list |
Let’s quickly summarize what we learned.
We learned,
|
| Assignment slide
Bulleted list |
As an assignment do the following.
Pick one Predictive AI example and one Generative AI example from your daily life. Write down one line explaining why each fits its category. Also, reflect on any risks or biases that may exist in your examples. |
| EduPyramids logo | This Spoken Tutorial is brought to you by EduPyramids Educational Services Private Limited, SINE, IIT Bombay. |
| Closing slide | Thank you for watching! |
