Mastering Prompt Engineering for GPT
Welcome to the exhilarating realm of Prompt Engineering! As our world becomes more and more intertwined with artificial intelligence and large language models (LLMs), understanding the nuances of crafting effective prompts is essential. This comprehensive course will provide you with the knowledge and techniques needed to harness the full potential of LLMs like GPT. Despite their immense capabilities, these LLMs work on a simple principle: You provide a sequence of text (the prompt), and the model generates a corresponding output. Yet, as the old computing adage goes, “Garbage In, Garbage Out.” A poorly designed prompt can result in disastrous outcomes, potentially jeopardizing your or your business's reputation. This course aims to ensure you avoid such pitfalls by teaching you the art of designing robust and effective prompts. Users have reported vastly different results when interacting with models like ChatGPT. The secret behind high-quality output lies not in luck, but in careful crafting of prompts, a set of skills that are as much art as science and that anyone can learn. This course, focused on the emerging field of Prompt Engineering, will enable you to unlock this hidden potential, and drastically improve the output you receive from these models. Throughout the course, you'll delve into a range of topics from understanding the core components of a prompt, exploring the various types and characteristics of LLMs, to tackling real-world use cases like text summarization, email writing, and business report generation. You'll learn how to improve and refine prompts, use examples to guide the model's responses, and address common issues such as bias and hallucination. Did you know that just by including a key specification in your prompt, you could vastly improve the response of ChatGPT? Or that, with a simple trick, you can turn ChatGPT into an expert in your chosen field, who can guide you and help you create content? These are just a few of the insights you'll gain as you progress through the course modules. Through hands-on labs, you'll get to interact with different LLMs, experiment with the OpenAI Playground, and use advanced prompt design techniques. We'll explore powerful methods like few-shot prompting, Tree of Thoughts (ToT) prompting, and persona-based prompts. So whether you're a tech enthusiast, an AI professional, or just someone curious about the world of AI and LLMs, this course is your gateway to mastering the art and science of prompt engineering. Equip yourself with the skills needed to navigate this rapidly evolving landscape and transform your interactions with these powerful models. Welcome aboard on this exciting journey of discovery and mastery in Prompt Engineering!
Objectives
- Understand the principles of Large Language Models (LLMs), including their capabilities, limitations, and underlying mechanics such as sequence prediction and prompt length.
- Define and design effective prompts using strategies like specificity, context provision, breaking complex prompts into simple steps, and chaining prompts for more refined outputs.
- Employ advanced prompt design techniques, including use of delimiters, asking for structured output, modifying the tone, and verifying model conditions.
- Apply techniques to mitigate common LLM issues, including hallucinations, biases, poor reasoning abilities, and mathematical limitations, ensuring high-quality output that respects ethical considerations.
- Utilize various prompting methods, including zero-shot, one-shot, and few-shot prompting, and understand how to provide examples to LLMs to guide the model's responses.
- Leverage tools like the OpenAI Playground for interacting with LLMs and gain practical experience in designing and refining prompts to suit various scenarios such as text summarization, email writing, and business report generation.
- Design and implement complex prompt strategies such as Tree of Thoughts (ToT) and persona-based prompts, expanding the utility and adaptability of LLMs for diverse use-cases.
Prerequisites
- A basic appreciation of AI technology
Contents
Introduction
- What is prompt engineering and why is it important?
- Key concepts in prompt engineering: prompts, Large Language Models (LLMs), and few-shot learning
- Core components of a prompt: context, instructions, examples
- Demonstration and discussion of prompt engineering examples
- Lab: Use peoples' jobs and/or hobbies and pose various problems to prompt.
Understanding the LLM
- Introduction to LLMs: Types and characteristics, including transformer-based neural nets and Reinforcement Learning from Human Feedback (RLHF).
- Sequence prediction, prompt length, and the context window.
- Characteristics and limitations of LLMs: always generates output, tendency to please people, hallucination, mathematical limitations, data training limitations, and conversation isolation.
- Lab: Interact with different LLMs to understand their workings.
Basic Prompt Design
- Importance of specificity and context provision in prompts
- Explaining ambiguous concepts and providing definitions
- Breaking complex prompts into simple steps or multiple prompts
- Strategies for prompt improvement: Iterating, refining, and chaining prompts
- Allowing the LLM to demonstrate reasoning and express uncertainty
- Lab: Improve given prompts according to specific requirements and design prompts to elicit particular information.
Giving Examples to LLMs
- Zero-Shot Prompting.
- One-Shot Prompting.
- Few-Shot Prompting.
- Lab: Practice giving examples to LLMs and observing the impact on responses.
Addressing Real-World Use Cases
- Designing prompts for text summarization, question answering, and creative writing.
- Writing an Email.
- Generating business reports.
- Lab: Designing business-focused prompts.
LLM Limitations and Strategies to Address Them
- Description, examples, and mitigation strategies for common LLM issues: Hallucinations, Bias, Poor reasoning abilities, Mathematical limitations, Source citation.
- Lab: Mitigating common limitations.
Using OpenAI Playground
- Introduction to the OpenAI Playground and its uses.
- How to get an OpenAI API key.
- Using the OpenAI Playground: Model selection, temperature setting, Top P, other parameters, and understanding system and user prompts.
- Lab: Using the playground.
Advanced Prompt Design
- Using delimiters to distinguish the data from the prompt
- Asking for Structured Output e.g. JSON, XML, HTML etc
- Modifying the tone of the output using style information
- Verifying model conditions
- Using successful task completion examples
- Providing step-by-step task instructions
- Instructing the model to devise its own solutions
- Lab: Utilising more advanced techniques to elicit the output you require.
Tools for Prompting and Prompt Design
- Overview of prompt design tools
- Lab: Hands-on with a selection of useful tools.
Tree of Thoughts Prompting
- Introduction and understanding of Tree of Thoughts (ToT) Prompting
- How to design Tree of Thoughts Prompts
- Lab: Designing ToT prompts
Persona-based Prompts
- Understanding Persona-based Prompts
- Designing Persona-based Prompts
- Lab: Creating a persona, testing it, utilising and refining it.
Conclusions
- Where to go from here
- Further resources
- Course wrap-up
- Throughout the course, periodic assessment and feedback sessions will be conducted to ensure the learning objectives are met.
