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 and use advanced prompt design techniques. We’ll explore powerful methods like few-shot prompting, Chain of Thoughts (CoT) 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!
Learning Outcomes
Upon completion of this course, participants will be able to:
- 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.
- Design and implement complex prompt strategies such as Chain of Thoughts (CoT) and persona-based prompts, expanding the utility and adaptability of LLMs for diverse use-cases.
Course Outline
Introducing Prompt Engineering
- What is prompt engineering and why is it important?
- Capabilities of ChatGPT tiers
- Key concepts in prompt engineering
- Core components of a prompt: context, instructions, examples
- Demonstration and discussion of prompt engineering examples
Prompting is Not Search
- Key differences from search queries
- Examples of effective prompts
Overview of Prompting Approaches
- A Taxonomy of Prompting: Reductive, transformative, and generative
Basic Prompt Improvements
- Importance of writing clear and unambiguous prompts
- Providing context and delimiters
- Structuring complex 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
Limitations of Language Models
- Description, examples, and mitigation strategies for common LLM issues
- Hallucinations and Mitigation Strategies
- Bias and Mitigation Strategies
Understanding Language Models
- Introduction to LLMs: Types and characteristics, including transformer-based neural nets and Reinforcement Learning from Human Feedback (RLHF)
- Attention mechanisms
- 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
Giving Examples
- When to use zero-shot, one-shot, few-shot prompts
Customising the Output
- Using templates effectively
- Using delimiters to distinguish the data from the prompt
- Asking for Structured Output e.g. JSON, XML, HTML etc
Role-Based Prompting
- Assigning roles for better responses
- Use case examples
Multiple Perspectives
- Simulating different viewpoints
- Improving decision making by taking into account multiple perspectives
- Converging on a concensus
Adding Personality
- Why add personality?
- Defining roles and personality traits
- Including anecdotes
Reasoning
- Improving reasoning capabilities
- Working with maths
Scenarios and Use Cases
- Data analysis example prompt
- Designing prompts for text summarization, question answering, and creative writing
- Writing an Email
- Generating business reports
Additional Topics
- More business use cases
- Leveraging custom GPTs
Conclusions
- Where to go from here
- Further resources
- Course wrap-up
Intended Audience
This course is suitable if you have had some exposure to ChatGPT and would like to deepen your skills. You can gain benefit from the course whether or not you have programming experience.
Prerequisites
Those attending this course should meet the following:
- Have used ChatGPT or other LLMs