Prompt Engineering: Meaning, Examples, and Best Practices

What is prompt engineering?

Prompt engineering is the process of enhancing the output of large language models (LLMs), such as OpenAI’s GPT-4, which power AI applications like ChatGPT.

This crucial aspect of machine learning and artificial intelligence (AI) involves carefully crafting input prompts to help the language model understand the context and produce desired results. This process requires creativity, understanding of the language model, and precision in formulating prompts for a specific task.

Effective prompt engineering techniques can improve model outputs, making AI tools more useful for complex tasks. Your choice of words and their sequence can change the quality and relevance of the generated content.

Chat-based LLMs have a conversation history, so they can also draw on previous conversations for context. This means you can create prompt chains to fine-tune the output even more.

Importance of prompt engineering in generative AI

Generative AI—AI systems capable of generating text, images, and other media based on input prompts from users—is great when it works. Still, generative AI algorithms can produce bad or incorrect results without the proper context. Even worse, they can be very convincing liars.

Prompt engineering helps you get around this issue by providing generative AI models with the proper context and information to produce high-quality results.

Some ways prompt engineering is important include:

• Providing control and intent. Help the AI understand your intent based on the input to control the response.
• Targeting desired response. Help the AI refine the output to keep it concise and in the right format.
• Mitigating bias. Avoid biases an AI may learn because of human bias in training data.
• Assuring coherence and relevance. Ensure the AI produces coherent results that are accurate and relevant to the user’s request.
• Optimizing user experience. Create a better user experience by creating great prompts behind the scenes to help users get the desired output without needing to test AI prompts themselves.
• Enhancing adaptability. Well-crafted prompts can help AI models adapt to various contexts and tasks, improving their versatility and usefulness across different applications.

Types of prompt engineering

Prompt engineering is writing text to feed to an AI model. However, several forms of prompt engineering can impact your success.

• Zero-shot prompting. This technique involves asking the model to perform a task without any examples or prior training on that specific task. It tests the model’s ability to generalize from its pretraining.
• Few-shot prompting. This method provides the model with a few examples of the desired task before asking it to perform a similar task. It can improve performance on specific tasks.
• Chain-of-thought prompting (CoT). This advanced technique encourages the model to break down complex problems step by step, mimicking human reasoning. It’s particularly useful for tasks requiring multistep reasoning.
• Fine-tuning and interactive prompts. This type of prompting helps you iteratively refine your initial prompt by looking at the AI response and making wording changes to improve the output and model performance. Fine-tuning also allows you to train the model to produce better output for a specific set of prompts.

Prompt engineering examples

LLMs and prompt engineering are useful in several real-world applications, so let’s cover some of the best ones.

Natural language processing tasks

One of the primary benefits of LLMs is their ability to read and analyze text. These models use natural language processing (NLP) techniques to understand the most important words in a text and what a text passage means.

Prompt engineers can use LLMs for various tasks, for example, to summarize the content of a news article or another section of text. NLP processing can pull the relevant information and present it as a bite-size chunk for the user.

Another unexpected benefit of language models is translation. Large models are effective at translating text. Well-defined prompts can produce better translations and help prompt engineers to craft applications that use them.

Chatbots and virtual assistants

One of the best parts about language models is their ability to take in new information. Without context, you’re limited to the training datasets from language models. This means the information might be several years outdated, depending on the model you use.

However, you can provide more relevant information by pulling updated data from a database. Modern LLM apps use vector databases to store information relevant to applications.

In the case of AI chatbots and virtual assistants, the application first looks at the vector database to pull related information. Prompt engineers can then use that data in their prompts to create conversational content.

For example, if a chatbot provides customer support, prompt engineers can update it with the latest information about products and services. This helps make sure customers get the help they need.

Or if a virtual assistant provides information about the weather, prompt engineers can update it with the latest forecasts to help people plan their day accordingly.

Content generation

Content generation is one of the most common tasks for prompt engineers. Their job is to coax the AI model to produce accurate information in the required format.

Good prompt engineering means telling the AI what type of output you want and how you want it presented. Let’s consider poem generation from an AI writing tool, for instance.

You may really like Edgar Allen Poe and want a poem in a style similar to his. If you tell the AI generator the topic of the poem you want and that you want it in the style of Edgar Allen Poe, the AI will do its best to fulfill that request.

Question-answering systems

Question-answering systems are another specific use case for LLMs. New applications allow users to ask simple questions in web apps and get a quick response.

In this case, prompt engineers don’t have much to work with. They have to determine the topic of a user’s question from limited data, find relevant information to add context to the LLM, and prompt the AI to create an accurate answer.

Take the case of a user asking for a country’s history. It isn’t in the power of generative AI to produce an entire history—it’s up to the prompt engineer to create prompts that encourage the AI to pack as much information into as few sentences as possible.

This type of system is useful for product companies—teach an AI about your product, and customers can use the product page to ask questions and get answers.

Language understanding and generation

You often need precise language when answering someone’s question. LLM models may struggle with complex fields like technology, medicine, and science.

Fine-tuning and prompt engineering play a vital role in making these systems work. Google’s Med-PaLM is a good example of this.

Instead of relying on base AI models, Google trained and fine-tuned a model with medical databases. The result produced a product that does an amazing job answering medical questions and understanding the user’s specific problems.

Recommendation systems

AI is playing an increasing role in recommendation systems for consumers. Research shows that 61% of shoppers are willing to spend more for personalized experiences, meaning you need content relevant to keep people doing business with you.

You can provide AI models of a shopper’s product preferences and generate recommendations based on those and what similar customers previously purchased.

You don’t have to stick with generic recommendations, either. Use feedback from product reviews and the product’s specifications to help generate text about why a product is worth buying and fits your customers’ needs.

Data analysis and insights

Generative AI models trained on proprietary data can offer an extra advantage to companies needing better data analytics.

You can build a base model on your own or fine-tune an existing model based on the information you have in your company’s databases. After you finish, you can tie existing and new applications to your data model and ask it questions about your data.

The medical field is one area that is growing in usage. Google released a new medical model added to Med-PaLM that helps doctors examine medical scans. Prompt engineering made it possible for doctors to ask questions to an AI about scans and get information—saving the time it takes to examine scans manually.

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Prompt engineering best practices

Now that you understand the basics of prompt engineering, how can you make the most of generative AI? We offer several best practices to help you create effective prompts.

Clearly define the desired response

LLMs are trained on a ton of data, so there’s room to misinterpret your input if you aren’t careful. The AI may go off track and show you irrelevant information. It may even generate overly creative and untrue responses known as hallucinations.

Clearly define the scope of your desired response in your request. Take the case of wanting to learn the life span of a historical figure. You might let the AI know you only want to know when the person was born and died, not any other historical information. This can help the language model confine itself to your request and create clear objectives within the AI.

Be specific and explicit

You don’t want to be vague when interacting with generative AI—it doesn’t actually understand what you’re thinking. The tool is doing its best to predict the most likely next character when generating responses.

Be specific with what you want your AI to deliver as output and give it clues to help it along. This can mean including output requirements in your prompt input and confining it to a specific format.

Let’s take the case of wanting to list some famous inventors of the 1800s and put them in a table. Tell the AI how many inventors you want to list and use table formatting to get that exact result.

Balance simplicity and complexity

A fine line exists between simplicity and complexity with AI models. A prompt that’s too simple won’t provide enough context and may result in vague or unrelated answers. On the other hand, a prompt that’s too complex can confuse the AI and lead to unexpected results.

Try to find a balance between simplicity and complexity. Give your AI enough information to determine what you want in simple terms to avoid overwhelming it with information.

This is especially important for complex topics where generative AI may struggle. You may confuse the AI if you include a lot of jargon in the prompt since it may not be as familiar with it. Instead, simplify your language and reduce the prompt size to make your question easier to understand.

Iterate and experiment

Prompt engineering is an iterative process. Since there are no rules for how the AI outputs information, you should test different ideas to see what kind of output the AI produces.

You won’t be able to do this in a few tries. You’ll need to constantly test AI prompts to see the results and iterate on your discoveries. Check for accuracy, relevance, and other elements to match your needs. More optimizing can help you reduce your prompt size (to decrease input cost) and generate better output.

Take the following example of a new sneaker.

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The AI has no information about the sneaker, so it makes stuff up. Let’s refine the prompt with more details.

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The second response is better and covers the features requested. But what if you wanted a more concise description?

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Constraining the output to 80 characters provides a much more concise description—perfectly usable for an e-commerce product page.

Train and evaluate the AI

You may need to do some additional training if your AI model isn’t producing great results. Many AI model providers offer the ability to fine-tune their models, meaning you can gather data and feed it into the system to create a custom AI model for your needs.

One good thing about training is that you train the AI on new patterns, which is a great way to train a model for results if you want a specific output format (such as a CSV format).

From there, you can create a prompt library to work with the newly trained AI model.

Prompt engineering FAQ

You might have questions about prompt engineering. We answer some of the most common.

Do I need a degree to be a prompt engineer?

Although there isn’t a strict degree requirement for prompt engineers, having a degree in a related field is helpful. Professionals with experience in computer science or data science may have an easier time understanding language models and crafting prompts.

Does prompt engineering require coding?

Knowing how to use programming languages like Python can be helpful when doing prompt engineering, but it isn’t necessary. Prompt engineering is more about understanding how language works and how to craft it to get the best results.

Is prompt engineering hard?

Prompt engineering doesn’t have the steepest learning curve, but it does require time to experiment with prompts to see what works. Take time to learn human and computer interactions and the capabilities of the AI models you interact with.

Find prompt engineering jobs on Upwork

Prompt engineering is a vital skill for businesses wanting to use generative AI technology in practice and freelancers wanting an assistant that can help them work through daily tasks.

If you’re a freelancer with time to master prompt engineering, browse Upwork’s latest prompt engineer jobs to find clients needing help.

Upwork is an OpenAI partner, giving OpenAI customers and other businesses direct access to trusted expert independent professionals experienced in working with OpenAI technologies.

Upwork does not control, operate, or sponsor the other tools or services discussed in this article, which are only provided as potential options. Each reader and company should take the time to adequately analyze and determine the tools or services that would best fit their specific needs and situation.This guide covers what you need to know about prompt engineering. You’ll learn what prompt engineering is, see how to use it to get better generative AI results, and review several examples of prompt engineering in use.