Generative AI Foundational Concepts - Part 1

Introduction to Generative AI

Generative AI refers to a class of artificial intelligence models capable of creating new content—such as text, images, music, or code—that mimics human creativity. Unlike traditional AI systems that analyze or classify existing data, generative models learn the underlying patterns and structures in data to produce original outputs.

The purpose of this blog is to provide a structured roadmap for understanding generative AI, focusing on key concepts and foundational knowledge that will help you navigate this exciting field. This is the first part of a series that will delve deeper into various aspects of generative AI.

Key topics to understand in Generative AI - Part 1

• What is Generative AI and Large Language Models (LLMs)
• How Generative AI works
• How to use Generative AI for:
  • Thought partner
  • Writing
  • Reading
  • Chatting
  • Bot triages for humans
• Limitations of Generative AI:
  • Knowledge cutoff
  • Hallucinations
  • Limited length of input and output
  • Limitations with structured, tabular data
  • Biases
• Tips for better prompting:
  • Be detailed and specific
  • Guide the model to think through the answer

Recommended Course

For those who want a structured learning path, I highly recommend the Generative AI For Everyone course on Coursera. This course is designed to provide a comprehensive introduction to AI, covering the essential concepts and technologies that form the backbone of modern AI applications. It's perfect for beginners and offers a solid foundation before moving on to more specialized topics.