This introductory course is designed for beginners with no prior knowledge of generative AI. You will start by gaining a high-level understanding of what generative AI is and how it works. Through interactive lessons and hands-on examples, you will learn fundamental skills like providing effective prompts and iteratively improving the generated outputs.
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As the course progresses, you will dive deeper into specific major generative AI models, including their unique capabilities and limitations. Finally,, you will get practical experience using leading systems like GitHub Copilot, DALL-E, and OpenAI to generate code, images, and text. By the end, you will have developed core knowledge to start experimenting with generative AI in a responsible and effective way for a variety of applications. This course aims to provide a friendly introduction to prepare complete beginners for further exploration of this rapidly evolving technology.
This course is part of the Large Language Model Operations (LLMOps) Specialization.
What you'll learn
- Learn to utilize Generative AI for automation.
- Develop Generative AI software solutions.
- Build solutions with Prompt Engineering to enhance Generative AI output.
Syllabus
Introduction to Generative AI
This week, you will learn what generative AI is and how it has evolved from early AI to the large language models used today. You'll understand how these models work in applications by learning about model architectures and the training process. The week provides an overview of major foundation models like ChatGPT and Hugging Face, highlighting their capabilities and limitations. You'll explore the generative AI landscape, comparing options like open source models, local models, and cloud APIs. By the end, you'll have a solid base of knowledge about the foundations of this technology and options for accessing and leveraging different AI systems.
Interacting with models
This week, you will learn the fundamentals of prompt engineering to interact effectively with generative AI models. You'll understand the concept of few-shot prompting and practice basic prompting techniques using context and examples. Building on this, you'll learn methods for improving prompts through personas, detailed instructions, and iteration based on feedback. Finally, you'll explore more advanced skills like breaking down tasks, chaining prompts, and other useful techniques to overcome context limitations.
Building robust Generative AI systems
This week, you will explore different types of generative AI applications, including API-based, embedded model, and multi-model systems. You'll learn the fundamentals of building robust applications using techniques like Retrieval Augmented Generation (RAG) to improve context. Through hands-on exercises, you'll gain experience testing an application locally and deploying it on the cloud.
Applications of LLMs
Here, you will learn the key capabilities of the OpenAI API. You will generate images with OpenAI’s DALL-E, “fine tuning” LLM models to Reddit questions and answers and summarize videos with OpenAI’s Whisper Model.
