Introduction to GenAI
Generative AI (GenAI) is a class of machine learning models designed specifically to generate new data that resembles the data it was trained on.
What is GenAI?
"Machine learning is making the models with all the math and research, GenAI is where you take already built models and modify them to cater to a particular use case."
"Big companies build superbrains. We build products that use them."
There are foundational models—like GPT (OpenAI), Gemini (Google), Claude (Anthropic), and LLaMA (Meta). These are massive, expensive to train, and general-purpose. They unlock billion-dollar platforms, and you get to build million-dollar applications on top of them.
Think of it like:
- OpenAI built the internet (the foundation).
- You build Shopify, Substack, Calendly, or any specific app on it.
| ` | Traditional ML | Generative AI |
|---|---|---|
| Goal | Analyze/predict from existing data | Create new data resembling the original |
| Input | Structured/tabular data, labeled | Text, images, video, code, speech, etc. |
| Output | Labels, scores, categories | New text, code, images, ideas, responses |
| Example | Spam detection, loan approval | ChatGPT writing an email for you |
Generative AI is so powerful because:
- You don’t have to train it from scratch
- You can fine-tune, prompt, or wrap it with tools and logic
- It becomes a brain-as-a-service
- It democratizes AI — you don’t need a PhD or 100 GPUs to build smart applications now.
So you're no longer thinking “Can I train a model to do this?”, you're thinking: -> “What can I build using this existing super-intelligence?”
How is GenAI Built?
- Underlying Tech: It's still machine learning — specifically deep learning (transformers, diffusion models, GANs).
- Training Process: Massive datasets + huge compute → Pretrained "foundation" model
- Inference: You give it a prompt, it returns novel content that fits that context.
Think of it like teaching an AI to understand language so well that it can speak with originality.
Prerequsites.
- We're expecting you understand basic progamming (preferably python.)
- Be openminded because it’s a shift in both tech and mindset — from model training to product building.
What This Course Covers
In this course, you’ll learn:
- Core Concepts: How LLM's works (without complex math).
- (Prompting Techniques)[/course/GenAI/different-prompting-techniques]
- Tools & Techniques: Adapting pre-built models (e.g., ChatGPT, Claude) and tools like Langchain, LangGraph, MCP servers, Retrival Augmented Generation (RAG) for your goals.
- Practical Applications: We've shared realworld project and product (business usecase) ideas.
- Tracing and monitoring AI applications
- Ethics & Responsibility: Using GenAI safely and fairly.
Whether you’re a Working Professional, CEO, artist, developer, or student—this course unlocks GenAI’s potential for your unique vision.
Referances
- GenAI with Python 1.0 course
- youtube channel - IBM Technology