Prompt Engineering with Generative AI Tools

Generative artificial intelligence (GenAI) can create new content, such as text, images, and music. It is powered by machine learning (ML) models that have been trained on massive datasets of existing content. Prompt engineering is the process of designing and crafting prompts that guide generative AI models to produce the desired output. You will start this course by learning how you can leverage prompt engineering to improve your day-to-day and work-related tasks. Next, you will see examples of prompting in action with external generative AI chatbots such as ChatGPT, Google Bard, and Microsoft Bing Chat. As several of these tools may not be supported on many corporate devices, you will not be expected to create accounts on those platforms, but you will be able to apply the learnings and principles to any corporate conversational AI chatbot in similar ways.

The OpenAI Playground is a web-based tool that lets you experiment with large language models (LLMs) to generate text, translate languages, write creative content, and answer your questions in an informative way. With the Playground, you can input text prompts and receive real-time outputs, and you can adjust hyperparameters to control the creativity, randomness, length, and repetition of the model responses. In this course, you will begin by creating an account to use the OpenAI Playground and you will learn how you are billed for its usage. Next, you will explore the different chat modes and models and work with the hyperparameters that allow you to configure creativity, randomness, repetition, and the length of model responses. You will also use stop sequences, which terminate the output when a specific phrase is reached, as well as the frequency and presence penalty, which penalize repetition of words and topics. Finally, you will learn how to view probabilities in generated text and explore how to use presets to share prompts and prompt parameters with other people.

Different types of prompts serve distinct purposes when interacting with language models. Each type enables tailored interactions, from seeking answers and generating code to engaging in creative storytelling or eliciting opinions. In this course, you will learn the four elements of a prompt: context, instruction, input data, and output format. Next, will also explore prompt categories, such as open-ended, close-ended, multi-part, scenario-based, and opinion-based. Finally, you will look at different types of prompts based on the output that they provide. You will use prompts that generate objective facts, abstractive and extractive summaries, classification and sentiment analysis, and answers to questions. You'll tailor prompts to perform grammar and tone checks, ideation and roleplay, and mathematical and logical reasoning.

Data generation prompts instruct language models to generate synthetic data, useful for creating datasets. Code generation prompts are used to produce code snippets or entire programs, aiding developers in coding tasks. Zero-shot prompts challenge models to respond to unfamiliar tasks, relying on their general knowledge. Few-shot prompts provide limited context to guide models in addressing specific tasks, enhancing their adaptability. You will start this course by working with data generation and code generation prompts. You will explore how to use starter code prompts, convert code from one language to another, and prompt models to explain a piece of code. Next, you will see how to leverage generative AI to debug your code and generate complex bits of code with step-by-step instructions. Finally, you will explore techniques to improve prompt performance.

Final Exam: Prompt Engineering with Generative AI Tools will test your knowledge and application of the topics presented throughout the Prompt Engineering with Generative AI Tools journey.