In this course, you will dive into the world of prompt engineering and discover the techniques and strategies to create powerful prompts that yield accurate and relevant responses from language models. Whether you are using ChatGPT, Bard, Claude, GPT-4, DALL-E, or Midjourney, understanding prompt engineering is crucial for optimizing your results. Throughout the course, you will learn the fundamentals of prompt engineering, explore advanced techniques for prompt optimization, and develop a testing framework to evaluate and refine your prompts. By the end of this course, you will have the necessary skills to engineer prompts that effectively communicate with language models and achieve your desired outcomes.

This course provides a comprehensive introduction to prompt engineering, a crucial skill for effectively utilizing large language models like ChatGPT and Bard. Discover the art and science of designing optimal prompts that elicit accurate and relevant responses from AI models. Learn techniques to enhance ad hoc prompting and master advanced strategies to improve performance and generate high-quality outputs. Gain insights into the iterative process of prompt testing and refinement. By the end of this course, you will be equipped with the knowledge and skills to engineer prompts that maximize the potential of language models and achieve desired outcomes.

This course teaches you the essential techniques and strategies for effectively prompting large language models. You will learn how to use Markdown, role play, be specific, keep one topic per thread, use examples, and regenerate responses. By mastering these prompting basics, you will be able to communicate your instructions clearly and get accurate and relevant responses from the models.

This course teaches you the essential techniques for formatting prompts and controlling the output of AI models. You will learn how to structure prompts effectively to get consistent and precise responses. Additionally, you will explore advanced formatting techniques such as using delimiters, requesting structured output, and leveraging variables and input/output tags. Furthermore, you will discover strategies for controlling the format of the output, including adversarial prompting defense techniques and the use of the Guardrails open-source project. By the end of this course, you will be able to create well-structured prompts and ensure secure and reliable outputs from AI models.

This course provides a comprehensive introduction to prompt chaining, a powerful technique for improving the outputs of large language models. You will learn what prompt chaining is, the different types of prompt chains, and how to effectively chain prompts to achieve desired results. By the end of this course, you will have the knowledge and skills to confidently apply prompt chaining in various scenarios.

This course will teach you the ins and outs of Few Shot Prompting, the leading way to customize GPT for your specific task or use case. You will learn what Few Shot Prompting is and how it differs from other methods like zero-shot prompting and fine-tuning. You will also discover when to use Few Shot Prompting and when to avoid it. Finally, you will explore best practices for implementing Few Shot Prompting, including determining the number of examples, adding few shots to your prompt, and crafting effective few-shot prompts for your business applications.

This course provides a comprehensive guide to prompt templating, a powerful technique that enhances the efficiency and effectiveness of AI prompts. You will learn how to incorporate placeholders and variables to create reusable templates, customize prompts for different contexts, and create a prompt library for easy access and organization. By the end of this course, you will have the skills to optimize AI models and improve the user experience.

This course explores the concepts of fine-tuning language models and AI-assisted prompting. You will learn how to refine pre-trained language models through fine-tuning and how to use AI models to generate effective prompts. By the end of the course, you will have the knowledge and skills to apply these techniques in various AI applications.

This course provides an introduction to chunking, a process of dividing large blocks of text into smaller segments. You will learn why chunking is important in AI document processing and how to develop effective chunking strategies. The course covers different methods of chunking and techniques for summarizing chunks of information. By the end of the course, you will have a solid understanding of chunking and its role in improving AI model performance.