Generative AI is a rapidly evolving field within artificial intelligence focused on creating new content. Unlike traditional AI that focuses on recognizing patterns or making predictions based on existing data, generative AI models learn the underlying patterns of input data and then generate new data that has similar characteristics. This can include text, images, code, audio, and more.
Key aspects often covered in introductory Generative AI courses include:
- Fundamentals of Large Language Models (LLMs): Understanding the architecture and workings of LLMs, which are a core component of many generative AI applications.
- Prompt Engineering: Learning how to craft effective prompts (inputs) to guide generative AI models and achieve desired outputs. This is a crucial skill for controlling and maximizing the potential of these models.
- Major Generative Models: Exploring different types of generative models, such as those used for text generation, image creation, code generation, and their respective capabilities and limitations.
- Applications of Generative AI: Discovering the wide range of applications across various domains, including automation, software development, content creation, data analysis, and more.
- Responsible AI Principles: Emphasizing the ethical considerations and responsible use of generative AI, including addressing potential biases, misuse, and societal impacts.
Introductory courses aim to provide a solid foundation for beginners, equipping them with the essential knowledge and practical skills to navigate the generative AI landscape. They often include hands-on examples, practical exercises, and projects to reinforce learning. Some courses also focus on specific applications, such as using generative AI for software development, business leadership, or data analysis.
