When thinking about updates to your product or improving how your employees interact with your systems, you are undoubtedly thinking about integrating AI into their workflows.

AI continues to transform the user experience in all industries, and the importance of designing for AI and understanding prompt engineering has become paramount.

Prompt engineering is crucial to the user experience of integrating large language models into your solution. Prompt Engineering is a process that involves designing and fine-tuning queries or instructions to elicit the most accurate, relevant, and valuable responses from an AI model. As AI models are generally trained on massive datasets, the role of Prompt Engineering helps shape the AI’s responses in a way that aligns with the user’s expectations.

The first step in designing for AI is to understand the AI model you’re working with and its limitations. While AI models like GPT-4 boast impressive capabilities, it is crucial to remember that they are not perfect. Recognizing the model’s limitations helps designers craft prompts that capitalize on its strengths while mitigating weaknesses.

Understanding a large language model like GPT-4 weaknesses requires research, experimentation, and adaptation. In this guide, we will take you through the journey of designing prompts to ensure the best possible user experience.

First, research GPT -4’s Performance Characteristics by familiarizing yourself with its strengths and limitations. Some of the common weaknesses include:

• A Limited reasoning ability: While GPT-4 can provide general answers, it may struggle with complex reasoning tasks or context-specific details.
• Sensitivity to input phrasing: GPT -4’s responses can vary significantly based on the wording of the prompt, which can lead to inconsistencies.
• GPT-4 generates wordy responses, which may not always be ideal for users seeking concise answers.
• Since GPT-4’s training data is cut off at a specific date, September 2021, it may not have the latest information on rapidly evolving topics.

Prompt Engineering is the tool that helps you overcome these shortcomings. Prompt Engineering experiments with different Prompt Styles to identify what is best for eliciting your desired responses. Some Prompt Styles include:

• Leverage open-ended questions instead of close-ended questions to encourage elaboration. Open-ended questions foster creativity and capture the nuances of the topic. On the other hand, use close-ended questions
• when you need a quick and straightforward response and to elicit factual data.
• Ask the model to think step-by-step and debate the pros and cons before generating an answer.
• Employ leading questions or specify the desired format for the response.
• When a single prompt isn’t yielding the desired results, consider using multi-step prompts to guide GPT-4 toward a more accurate response. For example, you can break down a complex question into smaller, simpler parts that the model can answer more effectively.

Once you identified the desired Prompts it’s time to test and refine them; just like any other MVP design, you want to try your Prompts with a diverse range of users and gather feedback to refine them further. Next, analyze the AI’s responses, identify areas where it excels or struggles, and adapt your prompts accordingly.

As your AI applications continue to improve, it’s essential to stay flexible and adapt your prompt engineering techniques accordingly. Be prepared to revise and refine your prompts as new iterations of GPT-4 or other large language models become available.

By understanding GPT-4’s weaknesses, experimenting with various prompt styles, testing and refining prompts, using multi-step prompts, learning from community practices, and continuously adapting your approach, you can effectively capitalize on GPT-4’s strengths and enhance the user experience.