Are you struggling to generate effective prompts for your language models? Do you find that the outputs you get are often not what you were hoping for? If so, you’re not alone. Many people have a misconception that language models like GPT-3.5 4.0 are magic boxes that can generate perfect outputs without any guidance. However, this couldn’t be further from the truth.

I want to explore the concept of prompt refinement and how it can help you get the most out of your language models. We’ll use the example of product management requirements to illustrate the process and provide you with some practical tips on how to refine your prompts effectively.

Prompt refinement is the process of iteratively improving your prompts to get the best possible outputs from your language models. It involves asking targeted questions, refining your prompts, and evaluating the outputs to see how well they meet your needs.

You will serve as my prompt refinement expert. Our collaborative goal is to develop the most effective prompt to produce the desired output. First, you’ll inquire about the topic of the prompt and pose guiding questions to ensure we’re aligned. With an initial understanding, you’ll present a preliminary prompt. Then, you’ll seek further clarification through targeted questions to refine the prompt. We’ll continue this iterative process until we’ve crafted the perfect prompt to generate the desired output.

Think of this as a prompt of prompts designed to help you create the very best prompt and maximise your chances of great output. While this example is generic, you can create a version that is specific to your role or the task at hand. I have created a library of different prompts for the various tasks I need to complete.

To illustrate the prompt refinement process, let’s use the example of a new social media feature that allows users to transcribe text using voice. Suppose you’re a product manager tasked with generating effective requirements for this feature. You could start with a preliminary prompt like this:

“Generate product management requirements for a new social media feature that allows users to transcribe text using voice. The requirements should prioritize accuracy, speed, and ease of use. The feature should also be scalable and adaptable to future changes.”

This prompt provides some basic guidance for the language model, but it’s still too broad and lacks specificity. To refine the prompt, you need to ask targeted questions that provide more context and clarify the requirements. For example:

  • Who is the target audience for this feature? Are they primarily professionals, students, or casual users? What are their pain and jobs to be done?
  • What are the key functionalities that should be prioritized for this feature? Should it support multiple languages, punctuation, and formatting options?
  • Are there any specific constraints or limitations that the language model should consider? For example, should the feature be able to transcribe speech in noisy environments or with different accents?
  • How can the feature be made scalable and adaptable for future changes? Are there any specific considerations that should be taken into account for the product roadmap?

By answering these questions, you can refine your prompt to something more specific and actionable. For example:

“Generate product management user stories to support transcribe text using voice that links to social media and allows young adults between the ages of 18 and 25 who are interested in fashion and beauty to to share ideas and their likes using voice. We should prioritise accuracy, speed, and ease of use, and should support multiple languages, punctuation, and formatting options. It should also seamlessly integrate with social media platforms such as Instagram and TikTok. The feature should be scalable and adaptable to future changes, such as the addition of new categories or functionalities.”

This prompt provides more guidance and specificity for the language model, resulting in more accurate and relevant outputs. However, it’s important to note that prompt refinement is an iterative process, and you may need to refine your prompt further based on the outputs you receive.

GPT-4.0 appears to be much better at self-reflection. If you ask it whether the answer it gave you was correct, it can often correct its own mistakes. Currently, I use it as a programming companion to help me get LangChain working in some of my experiments.

LangChain allows you to feed data from PDFs and create memories of previous conversations. This enables you to create your own agents. As business leaders, we are living in extraordinary times and must equip ourselves with new skills to leverage this amazing technology. More to follow on this soon

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.

%d bloggers like this: