[Mastering Prompt Engineering] Advanced Techniques and Latest Examples to Unlock the Potential of AI

[Complete Guide to Prompt Engineering] Advanced techniques and latest examples to unlock the potential of AI

Prompt engineering is an important skill for mastering generative AI and unleashing its true value. By creating appropriate prompts, AI can produce high-quality, creative output that exceeds our expectations. This article provides a comprehensive explanation of prompt engineering, from the basics to advanced techniques, the latest application examples, and ethical considerations.

What is prompt engineering? A thorough explanation of its importance and basics

Definition and Importance of Prompt Engineering

Prompt engineering is a technique for effectively designing and adjusting prompts given to generative AI models to optimize dialogue with AI. Prompts are important cues that help AI understand a task and generate appropriate output.

Generative AI generates content based on patterns learned from large amounts of data, but the output depends heavily on the prompt. Creating clear and specific prompts allows the AI to generate more accurate and creative output.

In recent years, large-scale language models (LLMs) such as ChatGPT have become increasingly popular, making generative AI more accessible. As a result, prompt engineering has become increasingly important. Prompt engineering is a necessary skill for effectively using AI.

Prompt Engineering Basics

To create an effective prompt, it’s important to remember these basic elements:

1. Clear instructions: Be clear about what you want the AI to do. It is important to avoid vague or abstract expressions and use specific, easy-to-understand words. For example, instead of saying “Tell me a funny story,” give specific instructions such as “Write a short, funny story that elementary school children can enjoy.”
2. Specific information: Provide specific information that AI needs to perform the task. By clearly communicating keywords, context, constraints, etc., you can get more accurate results. For example, to the prompt “Write an article introducing tourist spots in Osaka,” you can generate articles that better suit your needs by adding information such as “Family-friendly,” “Budget of 10,000 yen or less,” and “Hidden spots other than Universal Studios Japan and Osaka Castle.”
3. Desired output format: Specify the format in which you want the results to be displayed. Specifying the length of the text (short, long, etc.), the format (bullet points, table format, etc.), and the style (tone, writing style, etc.) will help you get the results you expect. For example, in the prompt “Tell me about cat breeds,” you can add the information “Please list 5 types in bullet points” to get the information in a more readable format.

In addition to these basic elements, the following techniques are also effective:

• Few-shot learning: By showing a small number of examples, an AI model can learn a new task. For example, for a translation task, by showing a few examples of the translation of words, such as “English: apple, Japanese: ringo,” the AI model can learn to translate other words as well.
• Chain-of-Thought prompting: By explicitly showing the thought process, you can improve the ability of an AI model to solve complex problems. For example, when solving a math problem, inputting the calculation process step by step can help the AI model to arrive at a more accurate answer.
• Zero-shot prompting: This technique instructs an AI model on a new task without providing any examples. The AI model performs the task based on the knowledge it has learned in advance. However, with zero-shot prompting, the AI model needs clearer and more detailed instructions to correctly understand the task and output the appropriate result.

Structuring prompts: Designing smooth interactions with AI

In order to create more effective prompts based on the basics of prompt engineering, it is important to structure the prompt. A structured prompt will help smooth the dialogue with AI and obtain more accurate and expected output. Here, we will explain three elements of structuring a prompt: clarifying roles, dividing tasks and dividing them into steps, and clearly stating constraints.

Clarification of roles

When creating a prompt, it is important to clearly define the roles of the AI and the user, so that the AI understands what role it is playing and generates the appropriate output accordingly.

• Clearly define the roles of the AI assistant and the user: For example, clearly specify the role of the AI, such as, “You are a professional translator. I will input Japanese sentences, and you will translate them into English.”
• Role-playing prompts: By having the AI play a specific role, you can create more natural conversations. For example, “You are a travel agent . I will tell you my travel preferences, and you can recommend a plan.” By setting the AI to a specific job or role, you can elicit more specialized knowledge and information.
• Collaboration of multiple AIs: By assigning different roles to multiple AIs and having them work together, more complex tasks can be handled. For example, a text generation AI can be linked with an image generation AI to generate images that match the content of the text.

Dividing tasks into steps

When instructing an AI to perform a complex task, it is important to break the task down into smaller steps, with clear instructions and expected outputs for each step. This will help the AI understand the task better and generate more accurate outputs.

For example, if you simply say, “Create a travel plan,” the AI will be confused about what to do. Therefore, by dividing the task into steps and giving specific instructions for each step, such as “1. Decide on a travel destination and dates,” “2. Book transportation and accommodation,” and “3. Plan tourist attractions and activities,” the AI can carry out the task more smoothly.

Clarification of constraints

You can set various constraints on the output that your AI generates. By clearly specifying the length, format, style, tone, and prohibitions of the output, you can make it easier to get the results you expect.

For example, if you simply say, “Please write a product description,” the AI will not know what length or style the text should be in. Therefore, by specifying constraints such as “The target audience is women in their 20s,” “Short sentences suitable for posting on social media,” and “Use a casual tone,” the AI can generate more appropriate text.

Ethical considerations are also important. Avoid discriminatory or offensive language, or instructions that infringe copyright. AI generates output based on training data, so if the training data is biased, the AI may also generate biased output.

Creating and Managing Prompt Templates

A prompt template is a template for a prompt that can be used repeatedly for a specific task. By creating a prompt template, you can create prompts efficiently without having to think of a prompt from scratch every time.

For example, it may be useful to create a prompt template like the one below.

• Blog Post Creation: “Write a blog post of about {word count} about {keyword} and aimed at {target audience}. Structure the post into the following format: {heading 1}, {heading 2}, {heading 3}…”
• Create an email: “Dear {recipient’s name}, I am contacting you regarding {subject}. \n\n{message}\n\nThank you in advance. \n\n{your name}”
• Image creation: “Draw {image content} in style {style} and size {size}.”

These prompt templates can be customized as needed, and you can also share and reuse effective prompts to improve prompt engineering skills across your team.

Parameter adjustment: Controlling the output results of the generation AI

The output of generative AI models varies greatly not only depending on prompts, but also on settings called parameters. By adjusting parameters, you can finely control the randomness, detail, style, and other aspects of the AI output. Here, we will explain the main parameters, how to adjust them, and points to note.

Temperature (output randomness)

Temperature is a parameter that controls the randomness of the generative AI’s output. It takes a value between 0 and 1, with lower values making the output more predictable and conservative, and higher values making the output more diverse and creative.

• Relationship between Temperature value and output result:
  • Values close to 0: Selects the most probable word or phrase, so the output is predictable and consistent.
  • Values closer to 1: A more diverse selection of words and phrases will be chosen, making the output more creative and unexpected.
• How to adjust the temperature and points to note:
  • Adjust the Temperature value according to your purpose. For example, if you want accurate information, set the Temperature low, if you want creative ideas, set the Temperature high.
  • The higher the Temperature, the more likely the AI will generate false information. If accuracy is important to you, you should set the Temperature lower or carefully check the output results.

Top-p (narrowing down output candidates)

Top-p (nucleus sampling) is a parameter that controls the randomness of the output of the generation AI, just like Temperature. It takes a value between 0 and 1. The smaller the value, the narrower the output candidates will be, and the larger the value, the wider the output candidates will be.

• Relationship between Top-p value and output result:
  • Values close to 0: Only a few of the most probable words or phrases are considered as output candidates, resulting in predictable and consistent output.
  • Values close to 1: Almost any word or phrase is a candidate, so the output will be more varied and unexpected.
• How to adjust Top-p and points to note:
  • As with Temperature, adjust the Top-p value according to your needs.
  • Top-p allows for finer control than Temperature, but finding the right value requires trial and error.

Presence penalty, Frequency penalty

Presence penalty and frequency penalty are parameters that suppress the repetition of certain words or phrases.

• Presence penalty: If a particular word or phrase is present in the prompt, it reduces the probability that that word or phrase will be included in the output.
• Frequency penalty: If a particular word or phrase has already appeared in the output, it reduces the probability that it will appear in the output again.

By adjusting these penalties, we can generate more natural and diverse sentences.

Other Adjustable Parameters

• Stop sequences: You can terminate sentence generation once a specific word or phrase has been output.
• Max tokens: You can specify the maximum length of the output text in tokens.

These parameters vary depending on the generative AI model. Please refer to the documentation for each model to set the appropriate parameters.

Advanced prompting techniques: Unleashing the full potential of AI

Once you have mastered the basics of prompt engineering and prompt structuring, it’s time to try some more advanced techniques. Here, we will introduce three techniques: utilizing system messages, linking with external tools, and countermeasures against prompt injection. By making full use of these techniques, you can further improve the capabilities of generative AI and enable it to handle more advanced tasks.

Using system messages

System messages are instructions to control the behavior of AI. Unlike prompts, they are not entered by the user, but are set in advance to dictate the behavior of the AI. System messages give you more control over the format, style, and tone of the AI’s output results.

For example, ChatGPT allows you to set system messages like the following:

You are a helpful, creative and very smart assistant.

You always respond in a safe and non-harming way.

By configuring this system message, ChatGPT will generate more helpful and creative responses, and it will also be instructed to respond in a safe and harmless way, reducing the risk of generating inappropriate content.

The appropriate content of system messages varies depending on the AI model and task. By understanding the characteristics of AI and setting appropriate system messages, you can maximize the capabilities of your AI.

Integration with external tools

Generative AI can be linked to external tools through API integration and plugins, allowing you to extend the capabilities of the AI and handle more complex tasks.

• Information acquisition and processing through API integration: By linking with web search APIs and database APIs, AI can acquire the latest information and generate text and images based on it. For example, an AI that creates travel plans can use a web search API to collect information on tourist spots and then use that information to suggest the best plan.
• Plugin extension: A plugin is software that extends the capabilities of AI. For example, ChatGPT’s plugin feature allows you to add functions such as a calculator, translation, and code execution, allowing ChatGPT to handle a wider variety of tasks.

Linking with external tools greatly expands the possibilities of generative AI. However, caution is required as the risk of security and privacy violations may increase depending on the tool you link with.

Prompt Injection Countermeasures

Prompt injection is an attack technique in which a malicious user intentionally embeds false information in a prompt in order to deceive an AI. Prompt injection can cause an AI to learn false information or generate inappropriate content.

The following measures are effective in preventing prompt injection:

• Input validation: Validate the prompts entered by the user to ensure they do not contain invalid strings or code.
• Prompt filtering: Block prompts that contain inappropriate words or phrases.
• Selection of training data for AI models: Care must be taken to ensure that the training data for AI models does not contain malicious prompts.

By taking these measures, you can reduce the risk of prompt injection and use AI safely.

Latest application examples of Prompt Engineering

Prompt engineering is evolving day by day, and its range of applications is expanding. Here we will introduce the latest examples of its applications in text generation, image generation, and other fields.

Text Generation

• More creative writing (poetry, novels, screenplays, etc.): Prompt Engineering is enabling AI to generate more creative writing. In creative activities such as poetry, novels, and screenplays, AI can partner with human creators to provide new inspiration and ideas.
  • Example:
    • OpenAI’s GPT-3 is capable of generating poetry and novels, and there are also projects underway to collaborate with human authors on creating works.
    • AI Dungeon is an AI-generated text-based adventure game where the player has free reign to navigate the story and the AI will generate a story to suit them.
• Generating copy in a specific style or tone (articles, ad copy, social posts, etc.): Prompt engineering allows AI to generate copy in a specific style or tone, making it more efficient for creating different types of writing, such as articles, ad copy, and social posts.
  • Example:
    • Jasper is an AI writing tool that generates SEO-friendly articles, ad copy, social media posts, and more.
    • Copy.ai is an AI writing tool that generates ad copy, blog articles, social media posts, and more, with over 90 tools and templates available.
• Multilingual support (translation, localization): Prompt Engineering enables multilingual support using AI. Not only does it improve the accuracy of translation and localization, it also enables natural translation that takes cultural nuances into account.
  • Example:
    • DeepL provides highly accurate machine translation services and is used for translating business and technical documents.
    • Unbabel offers a hybrid translation service using AI and human translators, enabling us to deliver high-quality translations quickly.

Image Generation

• High-quality image generation (photos, illustrations, artwork, etc.): Prompt Engineering enables high-quality image generation using AI. It can generate images in a variety of styles, including photos, illustrations, artwork, etc.
  • Example:
    • OpenAI’s DALL-E 2 is an AI that can generate high-quality images from text. Artists use DALL-E 2 to visualize unrealistic landscapes and abstract concepts, or to transform existing artwork into a new style.
    • Midjourney is an AI service that can generate images from text on Discord. It excels at creating beautiful images like works of art, and is used by many artists and designers.
• Generate images in a specific style or composition (anime, watercolor, portrait, etc.): Prompt engineering makes it possible to have AI generate images in a specific style or composition, allowing you to generate images in a variety of styles, such as anime, watercolor, portrait, etc.
  • Example:
    • Artbreeder is a tool that uses AI to synthesize and edit images. You can freely combine facial features, hairstyles, expressions, etc. to create original characters.
    • Dream by WOMBO is an app that uses AI to transform photos and paintings into a dream-like style.
• Image editing and transformation (background removal, object addition, style transfer, etc.): Prompt Engineering enables AI-powered image editing and transformation. It can automate various editing tasks such as background removal, object addition, and style transfer.
  • Example:
    • RunwayML provides an AI-powered image editing tool with features such as image background removal, object tracking, and style transfer.
    • VanceAI is a tool that uses AI to improve image resolution and remove noise.

others

• Music Generation (Composition, Arrangement, BGM Generation): Prompt Engineering enables AI-based music generation. It can automate various music production tasks such as composition, arrangement, and BGM generation.
  • Example:
    • Amper Music is an AI composition tool used to create background music for movies and games.
    • Jukebox is a music generation AI developed by OpenAI that can generate music in a variety of genres.
• Video Creation (Video Editing, Adding Effects, Generating Animations): Prompt Engineering enables AI-based video creation. It can automate various video production tasks such as video editing, adding effects , and generating animations.
  • Example:
    • RunwayML provides AI-powered video editing tools with features such as video background removal, object tracking, and style transfer.
    • DeepMotion develops AI-powered motion capture technology that can generate animations that realistically reproduce human movement.
• Code generation, data analysis: Prompt Engineering enables AI to generate code and perform data analysis. Simply give instructions in natural language and the AI can generate code or perform data analysis.
  • Example:
    • OpenAI Codex is an AI that generates Python code from natural language, helping to improve programmer productivity.
    • Tableau is an AI-powered data analysis platform that allows you to perform data analysis easily with drag and drop.
• Game development, education: Prompt Engineering is also used in the fields of game development and education. It can automate a variety of tasks, such as automatically generating game characters and stages, and creating educational content.
  • Example:
    • Promethean AI provides AI-based game development tools that can automatically generate game characters and stages.
    • Duolingo is an AI-powered language learning app that offers personalized study plans and real-time feedback.

Caution and Ethics of Prompt Engineering

Prompt engineering is a powerful tool for maximizing the power of generative AI, but it must be used with care. Here we take a closer look at the precautions and ethical issues involved in prompt engineering.

Limitations and biases of AI

Generative AI only generates output based on training data, so its capabilities are limited.

• Reliability of information: Generative AI may generate information that is not factual or is incorrect. In particular, when it comes to specialized knowledge or the latest information, you should not blindly accept the output of the AI, but always check it with a reliable source.
• Bias in training data: AI may reflect biases contained in the training data. AI trained with data that contains prejudices against a particular gender, race, culture, etc. may generate discriminatory output. To mitigate bias, it is important to train using diverse datasets and develop algorithms that detect and correct bias.

Prompt abuse and countermeasures

Prompt engineering can also be used for malicious purposes.

• Generating fake news, spam, and hate speech: Generative AI can be misused to generate harmful content such as fake news, spam emails, and hate speech. To prevent such misuse, it is necessary to develop technology to filter the output of AI and to detect malicious prompts.
• Security measures: There are also concerns about cyber attacks that exploit vulnerabilities in generative AI. For example, attacks could be made to input malicious prompts into the AI, causing the system to malfunction or stealing personal information. It is important to strengthen the security measures of generative AI systems and fix vulnerabilities.

Ethical considerations

Ethical considerations are also essential when conducting prompt engineering.

• Personal information protection: When handling personal or confidential information, sufficient consideration must be given to privacy protection. If personal information is included in the AI learning data or output results, appropriate processing such as anonymization must be performed.
• Copyright infringement: Generative AI may learn from existing works and generate content that infringes copyright. As there are no clear rules yet regarding copyright for content generated by AI, caution is required.
• Compliance with ethical guidelines for AI: It is important to comply with ethical guidelines regarding the development and use of AI. For example, refer to guidelines such as “Responsible AI” by the Partnership on AI and “Ethically Aligned Design” by IEEE, and be sure to develop and use AI ethically.

Summary: Mastering generative AI with prompt engineering

Prompt engineering is a powerful tool for unlocking the full potential of generative AI. By crafting the right prompts, AI can produce outputs that exceed our expectations, bringing innovation to business and creative endeavors.

However, there are various points to note about prompt engineering, such as the limitations of AI, bias, misuse, and ethical issues. By keeping these points in mind and practicing responsible prompt engineering, generative AI will be a force that enriches our society.

Prompt engineering is still a developing field, and we expect to see new techniques and application cases emerge in the future. By constantly collecting the latest information and continuing to hone your prompt engineering skills, you will be at the forefront of generative AI and maximize its potential.