What is Prompt Engineering?

In the 20th century, computers changed the world. Now, generative AI is changing the world.   

Artificial Intelligence has become so powerful that it can benefit almost every field. Every business and organization is looking to use AI to boost productivity and growth.  

AI is powerful. However, it can’t provide the best result without proper input. The output of AI depends on the input. AI provides unrelated results without effective prompts. 

Here, prompt engineering magic begins. It is the process of creating the most effective prompt. It produces the best results that meet user expectations. 

In this article, we will discuss what is prompt engineering, its application, how to craft effective prompts, and more. 

So, let's learn about prompt engineering.     

What is Prompt Engineering? 

You should know about prompt first if you want to learn prompt engineering. 

So what is prompt exactly? 

Prompt is the input text of generative AI. It tells the AI model what to do. Thus, the AI models’ output depends on the prompt. You won't get the desired results without effective prompts. 

To get the desired result, you should write prompts that tell exactly what the AI model should do. The process of creating this type of input is called prompt engineering. Prompt engineering involves applying different prompt techniques.  

Prompt engineering helps AI models understand input precisely, which results in accurate and precise output. 

Technical Aspects of Prompt Engineering

Prompt engineering is not just mixing different modifiers, text, and techniques. It is about creating prompts that deliver the best output. To write the best prompt, you should understand some technical aspects of prompt engineering. 

NLP (Natural Language Processing)

NLP is the heart of artificial intelligence. It enables communication between humans and AI. It helps AI models to understand, analyze, and respond to human language contextually. AI model’s output directly depends on its proficiency in NLP. The higher the proficiency, the more accurate the results it produces. 

LLMs (Large Language Models) 

LLMs are an advanced part of artificial intelligence. Enormous amounts of datasets train them to predict the next word in a sentence. They guess the next word in a sentence based on the preceding words. It enables them to understand the context effectively which helps to produce meaningful text.     

Transformers

Transformers are the basics of many large language models, including ChatGPT. They are specific types of neural networks and specialized at handling sequential data such as language. 

Transformers are good at understanding sequential relations between words within a sentence. There is a special element in Transformers called 'pay attention'. It helps LLMs to figure out the most important task in the input text.  

Parameters

Parameters are variables of AI models. During training, AI models learn the variables from training data. Prompt engineers don’t adjust these directly. However, understanding them could help you understand why AI models respond to prompts in a certain way.    

Tokens 

Tokens are units of text that AI models read and understand. They could be from a character like B to a word like Power. LLMs can handle a certain number of tokens. This is why prompt engineers need to know the limits of an AI model. It enables creating longer queries and gives longer input data. 

Multimodality  

Multimodality is the new trend in the AI world. It can understand, interpret, and generate different types of data. Multimodality can generate text, images, code, etc. 

It is crucial for prompt engineering. It enables prompt engineers to craft prompts that can generate diverse output. 

A clear understanding of these technical concepts helps you create effective prompts. 

How does Prompt Engineering Work?

Prompt engineering optimizes generative AI’s input. It shapes the AI model’s output. It uses different techniques to create input for getting the best output from AI models.  

AI models uses transformers arhcitechture. They process vast amounts of data through neural networks. But they have limitations. Prompt engineers create input, keeping in mind transformers’ limitations. They create input in such a way that AI models understand it precisely. 

Large Language Models(LLMs) are the backbone of generative AI. They use natural language processing(NLP) to understand input. Prompt engineering creates prompts using simple natural language that ensures the best output.            

Prompt engineering works similarly to communication works between human beings.

Related Read: How Does Artificial Intelligence Work?

Importance of Prompt Engineering

Prompt engineering offers many benefits. Here are some obvious reasons for using prompt engineering; 

Boosting output accuracy

AI models's accuracy is a crucial matter. Prompt Engineering helps boost AI models' output accuracy. It directs AI models toward relevant information through focused instruction. This provides control over generation and ensures domain-specific relevance. As a result, it boosts output accuracy. 

Mitigating misinterpretation and ambiguity

Ambiguity and misinterpretation are problems of AI models’ output. Precisely crafted prompts can mitigate these issues. Prompt Engineering helps you create precise prompts, thereby reducing misinterpretation and ambiguity.     

Getting the most out of the AI models

What matters most when it comes to the performance of the AI models?

It is prompt!

You won't get the best output from AI models without effective prompts. Prompt engineering helps you to create precise prompts, resulting in your desired output. 

According to a case study, prompt engineering boosts ChatGpt precision by 6%. It also outperforms other AI models.        

Empowering employees 

Prompt Engineering enables you to create prompts that analyze tasks and recommend improvements. It helps employees further improve their work quality. It also makes employees more efficient.        

Applications and Examples of Prompt Engineering

Prompt engineering has applications in many fields. Here are 8 applications of prompt engineering;   

1. Content Generation

Undoubtedly, the content-producing sector is heavily influenced by generative AI. 73% of companies use generative AI to produce content. 

Prompt engineering has a wider application in content generation. For example, it can help you generate web copy and ad copies. You can generate blog posts, outlines, brainstorm ideas, create video scripts, etc. using AI. 

It can generate social media posts for your products and services. You can also write meta descriptions and generate SEO friendly titles for your blog posts. 

Here, we are using ChatGPT to generate an outline of a blog post. Let's see how it responds! 

The Prompt:

Create an SEO-friendly outline for a blog post on the topic of 'sustainable living practices for urban dwellers.' The target audience is young professionals living in city apartments who are interested in reducing their carbon footprint without sacrificing convenience. The primary keyword is 'sustainable living for urban dwellers,' with secondary keywords including 'eco-friendly city living,' 'urban sustainability practices,' and 'green living tips for apartment dwellers.' Include sections on energy saving, sustainable transport, green products, and waste reduction tips.

ChatGPT Generated Output:

2. Question and Answering

When it comes to questions and answers, AI models have effective applications. It enables users to ask questions on the website and get accurate answers. 

For example, think you want to know the answer of a question. You are trying different AI tools to get your answer but you are not satisfied with it. You think the tool is not generating what you want. 

What's the reason?

Perhaps, you are not providing the perfect prompt. 

Now, we'll use Genimi AI previously known as Google Bard to answer a question. Let's see what it generates. 

The Prompt: 

Please provide a detailed explanation of the impact of artificial intelligence on job automation, including how it affects various industries and the potential for new job creation. For example, if relevant, could you also touch on the role of AI in the healthcare and automotive industries? I'm particularly interested in examples of specific jobs that are most at risk and those that might emerge as a result of AI advancements.

The Answer Generated by Gemini AI

Related Read: How to Use Google Bard AI (Now Gemini)

3. Language Translation

Language translation was tough. 

Yes, You are reading right. 

However, the situation has changed thanks to LLMs. They take language translation to the next level. LLMs use the Natural Languages Process. It enables them to translate language effectively and efficiently. 

Translating text into other languages using LLMs is a simple process; you only need to ask the model to do it. 

If you write a perfect prompt for translating your content, the AI platforms will translate them instantly into your preferred language.

Let's see an example. We have provided a Chinese poem to ChatGPT and told to translate it into English. 

Here is the output: 

4. Chatbots and Conversational Agents

LLMs are powerful, but they have limitations. You are limited to their training data. It means their information could be outdated. Also, they don’t have access to the latest information. 

But the good part of the large language model is you can feed them information. You can provide them with relevant information from the vector database. LLMs use vector databases for storing relevant information.

Chatbots and conversational agents get relevant information from the vector database. Prompt engineers can use this information in prompts to create conversational content. 

For instance, if an AI chatbot provides customer support service, prompt engineers can update it with the latest product information. It will help customers to get the latest product information.  

Related Read: What is Conversational AI?

5. Code Generation and Programming Assistance

LLMs have become great ai tools of developers. They can help to build applications from scratch. You just need to ask; AI will write the code for you. 

Prompt engineering takes AI models’s coding capability to the next level. With a precise prompt, you can generate missing code. In addition, you can debug code and create an algorithm. You can even upload a screenshot of an application, and AI models will generate code for you.     

The most amazing part is that, you can now creare a complete website with AI without writing a single line of code. Thanks to the top AI website builders that can generate a full website using artificial intelligence. 

Dorik AI, is the best AI webiste builder in the industry. It can generate a full responsive website with just a single prompt. Plus, it populates your site with engaging web copies and generate pixel perfect images. You can even regenerate the website's content, any specific section, or the full layout by writing prompts.

Here, the point is, the more detail your prompt will be, the more accurate and engaging your website will be. 

6. Data Analysis

Data analysis is a complex task. It requires coding expertise. 

Prompt engineering empowers non-coders in data analysis tasks typically requiring coding skills. This allows data analysts to focus on extracting insight instead of being busy with code syntax. 

Prompt engineering helps data analysts prototype and explore data analysis techniques quickly. This empowers them to iterate and experiment efficiently. As a result, helping data analysts to find out the most effective method for specific data analysis needs. 

The best example of prompt engineering applications in data analysis is in the medical field. Prompt engineering enables doctors to ask AI models like Med-PaLM 2 about scans and get answers. 

7. Recommendation

Personalization has a solid impact on the success of e-commerce and retail businesses. 88% of businesses boost their sales significantly by using personalized approaches. 

Generative AI and prompt engineering can help businesses provide personalized experiences. 

How?  

AI recommendation systems can provide personalized recommendations to customers. These systems analyze customers' browsing history, purchasing history, and click-through rate to understand behavior. After that, it recommends the best products based on the customer behavior.    

8. Human Resources and Recruitment

Prompt engineering has revolutionized the human resources and recruitment field. It offers a wide range of applications in the HR field. It improves HR professionals' work efficiency and saves time. 

It helps HR professionals screen job applications using custom prompts. For example, provide your employee requirements and job applications, then ask AI for the most suitable applicant. Then, see the magic; AI models will show you the best applicant according to your requirements.  

HR professionals can analyze employees' performance using custom prompts. It also helps HR professionals in developing training materials. Additionally, it helps to create employee onboarding programs.

Best Prompt Engineering Practices 

Prompt engineering techniques and strategies help you to write prompts that produce the best output. Use these 5 prompt engineering best practices while writing prompts; 

1. Clearly Define the Desired Response

What will happen if you want to buy Colgate toothpaste for sensitive teeth and ask the shop owner for only Colgate toothpaste? 

The shop owner could give you any Colgate toothpaste. To get the sensitive one, you must ask for Colgate-sensitive toothpaste.

Similarly, if you want to get the desired response from AI models, you must clearly define it. 

For example, you want an AI model to write an essay in 200 words on mangoes that describes its history, nutrition, and health benefits.  

So, what will be the best prompt in this case?

You should use the prompt “Write an essay on mango that describes its history, nutrition, and health benefits in 200 words” instead of just “Write an essay on mango in 200 words”. 

2. Use all Prompt Elements When Writing Prompts

A Prompt has several elements. To write the most effective prompt, you must include them in the prompt. 

Instructions are an important element of the prompt. It tells the AI model what it should do using assertive statements. Usually, prompts start with instructions as they consist of the central motive of the prompts.     

For instance, if you want to simplify a paragraph, instructions could be “simplify this ‘Paragraph’ paragraph so that it is easy to read.” 

Another prompt element is input data; it is operable information. In the example, the paragraph is the input data. 

The context element provides extra information for AI models to complete the task. In the example, context is ‘so that it is easy to read’. 

The output indicator is the last element of the prompt. It guides the fundamental structure of the output requested in the prompt.

3. Use Prompting Techniques

Anything worth doing requires a variety of methods and approaches. In the same way, to write effective prompts you should use various strategies and techniques. Here are some techniques to help you write the best prompts:

Zero-shot prompting: This is a simple technique that works best for easy tasks. It doesn't provide the AI model with any extra data; it only asks questions. 

Few-shot prompting: This method provides the AI model with extra data. It provides examples as additional information. For difficult tasks, few-shot prompting is quite helpful. 

Chain-of-thought prompting: It differs from other techniques. It breaks down reasoning into simpler intermediate steps to enhance the performance of LLMs. 

Prompt chaining: This technique divides complicated tasks into smaller ones. It then uses the output from LLMs to finish the primary task. It performs well on tough tasks.

4. Ensure Prompts are not too Simple or Complex

What is the best prompt?

Prompt that is not so simple or complex. The perfect prompt is between simplicity and complexity. You should always make sure that your prompt is not too simple or complex. 

Why?

If the prompt is too simple, it won’t provide the AI model with enough information. Thus, it will provide unrelated results. On the other hand, if your prompt is too complex, it will confuse the AI model. As a result, it won’t provide the desired result. 

So, to get the best output from AI models, keep a balance between simplicity and complexity. 

5. Train and Evaluate the AI Models

You may have crafted great prompts, but your AI model is not providing the desired result. 

So what is the solution?

You need to train the AI model with additional information. The good news is many AI model providers allow you to train their model. It means you can collect and feed new information to the AI model and create a custom AI model.  

After creating a custom model, try different prompts and evaluate the performance. Keep the prompts that provide the best result and make a prompt library to work with the new AI model.   

Conclusion

Prompt engineering is not so easy or difficult. You can craft effective prompts if you know what it is and its technical concepts. 

Prompt engineering has various applications, from text generation to e-commerce product recommendation. You can use it in every field that uses generative AI.

You won’t get the desired output if your prompt is ineffective. Though prompt engineering is not tough, it needs some practice. Use all the prompting techniques described in this article while writing the prompt.