The Science of Prompt Engineering

Introduction

In the rapidly evolving landscape of artificial intelligence, the art of creating precise and effective prompts, known as prompt engineering, has emerged as a crucial skill for harnessing the full potential of AI models. As these models become integral to various industries, from customer service to content creation, the quality of their responses is highly dependent on the prompts they receive. This article dives deep into the essence of prompt engineering, providing insights into best practices, structures, and the most effective prompts to ensure that AI delivers accurate, relevant, and impactful results. Understanding the nuances of prompt engineering is essential for optimising AI interactions and outcomes.

First, let us understand what exactly is prompt engineering. Prompt engineering is the process where you guide generative artificial intelligence (generative AI) solutions to generate desired outputs. Even though generative AI attempts to mimic humans, it requires detailed instructions to create high-quality and relevant output. In prompt engineering, you choose the most appropriate formats, phrases, words, and symbols that guide the AI to interact with the users more meaningfully.

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Now think about this, the AI model is trained on a gigantic amount of data, for reference, the GPT 4 is trained over 100 trillion parameters! For a daily user who wants some specific information, the AI model can just not guess what the user is looking for in the vast training data. Therefore, the quality of the output from the model really depends on the quality of prompt we provide. The less the model has to guess at what you want, the more likely you’ll get it.

Techniques for the Best Prompt

To create the perfect prompt, one can use various techniques and tactics which will help them get the best outputs from the model.

Write Clear Instructions

Writing clearer instructions will automatically make the response more personalised and less generic. If you really want the model to showcase it’s creative abilities or want very detailed and informative responses, a detailed prompt can make a difference.

For example:

• Basic prompt: Write me a program to calculate the sum of first n natural numbers
• Better prompt: Write me a program in Python to calculate the sum of the first n natural numbers in the most efficient way possible. Also, explain what each block of code does and why it is written they way it is.

Another way to improve the prompt is to adapt a persona. By adapting a particular persona, the response is framed in a manner such that the tone and vocabulary used matches the persona.

For example:

• Basic prompt: Write a leave application
• Better prompt: Adapt the persona of a class XII student and write a leave application to be submitted to the headmistress of the school. The reason for leave is to visit relatives place for 1 week's time.

Additionally, one can use delimiters such as "sample text" in the prompt which can help demarcate sections of text to be treated differently.

For example:

• Basic prompt: Here are the arguments of a debate which are for and against the motion AI will lead to job loss respectively. Summarise the points for each of them.
• Better prompt: You will be provided with a pair of articles (delimited with "") about the topic 'AI will lead to job loss'. These are arguments of a debate which are for and against the motion. First summarise the arguments of each article. Then indicate which of them makes a better argument and explain why. "insert first article here" "insert second article here"

Specifying the steps required for completion of a task is also one tactic which can help improve the responses from the model.

For example:

• Basic prompt: Summarise the following text in one line and translate it into Spanish.
• Better prompt: Use the following step-by-step instructions to respond to my inputs.

Step 1 - I will provide you with text in triple quotes. Summarise this text in one sentence with a prefix that says "Summary: ".

Step 2 - Translate the summary from Step 1 into Spanish, with a prefix that says "Translation: ".

Next, one can include examples for the AI model to learn and then implement the examples into the response. If you intend for the model to copy a particular style of responding to user queries which is difficult to describe explicitly, this is known as "few-shot" prompting.

For example:

• Basic prompt: Write a paragraph about oceans in a poetic way.
• Better prompt: Write a paragraph about oceans in a poetic way. One sample paragraph which was written for patience is as follows: "The river that carves the deepest valley flows from a modest spring; the grandest symphony originates from a single note; the most intricate tapestry begins with a solitary thread." Write the paragraph on oceans in a similar way.

You can ask the model to produce outputs that are of a given target length. The targeted output length can be specified in terms of the count of words, sentences, paragraphs, bullet points, etc. It is important to note that instructing the model to generate a specific number of words does not work with high precision. The model can more reliably generate outputs with a specific number of paragraphs or bullet points.

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Provide Reference Text

Language models can confidently invent fake answers, especially when asked about esoteric topics or for citations and URLs. In the same way that a sheet of notes can help a student do better on a test, providing reference text to these models can help in answering with fewer fabrications. If we can provide a model with trusted information that is relevant to the current query, then we can instruct the model to use the provided information to compose its answer.

For example:

• Basic prompt: Answer the following questions asked by the user about the best stocks to buy right now(ChatGPT 4o is not updated daily and hence would not be able to suggest the best stocks to buy at the moment)
• Better prompt: Use the provided analysis of the latest trends in the stock market delimited by triple quotes to answer questions. If the answer cannot be found in the analysis, write "I could not find an answer." Also explain the reason for your answer. This will help me in understanding the market better.

Conclusion

Crafting well-designed prompts is not merely a technical skill but an essential art that shapes relevance of AI responses. By understanding the principles of prompt engineering and by learning from real-world applications and future trends, we can unlock the true potential of AI models. Whether for improving customer interactions, driving creative endeavours, or enhancing analytical capabilities, effective prompt engineering stands at the forefront of AI innovation.