What is AI & Generative AI?
Artificial Intelligence (AI) is the field of computer science that enables machines to perform tasks that usually require human intelligence — like decision-making, language understanding, visual perception, and problem-solving.
Generative AI is a powerful subset of AI that focuses on creating new content — such as text, images, audio, or code. Instead of just analyzing data, it can generate new data. Tools like ChatGPT, DALL•E, and Midjourney are real-world examples of Generative AI in action.
✨ With Generative AI, machines don’t just think—they create!
Basics of AI, ML, and NLP:
To understand AI deeply, it’s important to explore its three core components:
1. AI (Artificial Intelligence):
The broader concept of machines being able to carry out smart tasks.
2. ML (Machine Learning):
A subset of AI where machines learn patterns from data and make predictions or decisions without being explicitly programmed.
3. NLP (Natural Language Processing):
This is how machines understand, interpret, and respond to human language. It’s what powers voice assistants, chatbots, and AI-generated content.
Introduction to LLMs (Large Language Models):
Large Language Models (LLMs) like GPT-4 or Google’s PaLM are advanced AI models trained on huge amounts of text data. These models:
• Understand context
• Generate human-like text
• Translate languages
• Summarize information
• Answer complex questions
LLMs form the backbone of modern Generative AI tools. LangChain uses these LLMs to build intelligent, dynamic AI applications — like AI-powered chatbots, automation tools, and decision systems.
Why Learn This Module?
Understanding the foundation of AI, ML, NLP, and LLMs helps you:
• Build a strong conceptual base
• Stay ahead in the AI-driven future
• Prepare for practical projects using LangChain
Whether you’re a developer, data enthusiast, or tech entrepreneur, this module equips you with the knowledge to confidently enter the world of Generative AI & LangChain.
Evolution of AI: From Rule-Based to Generative AI
• Brief history of AI: Symbolic AI → Machine Learning → Deep Learning → Generative AI
• How AI evolved to understand context, learn from data, and now generate content
• Real-world examples of each stage
Applications of Generative AI in Real Life
• Content creation (blog posts, design, marketing)
• Customer support (AI chatbots like ChatGPT)
• Healthcare (medical report summaries, diagnostics)
• Education (AI tutors, smart assessment tools)
• Coding & Development (AI code generators)
