What is Prompt Engineering?

What Is Prompt Engineering?

Prompt engineering refers to the input (text) that is prepared for a specific AI task. Such input is usually a text (a simple question or complex composition of several input elements, such as context, examples, rules for output). A well-written prompt helps the prompt engineer generate high-quality answers. If the prompt is not written well enough, the model will fail to complete the task or even produce incorrect results.

An example of the former type of prompt is “write about cybersecurity for a general audience”. This can be developed into more specific prompts such as “write a short essay listing the main types of cybersecurity risk faced by small businesses and explaining each in simple terms”. The prompt could also specify output in the form of paragraphs and include examples of each point.

Importantly, prompt engineering involves more than writing great questions for AI. Most importantly, one has to test different versions of a prompt and based on the results improve and fine-tune it, so the AI can consistently deliver the desired results. The quality of the prompt greatly increases as more and more tasks are shifted to AI.

How Does Prompt Engineering Work?

First, define what you want the input to produce. This means you have to decide what you want the AI to create. For whom are you creating this? What are the limits of the output?

An example prompt for AI writing about a customer’s needs for a business (such as a manager of a small retail store) could include: Context, Audience, Sources, Word Count, Output Format, Writing Style and more.

The review phase contains things that you missed in the creation of the prompt such as important details or misunderstandings. Also be aware of restrictions on formatting that you placed on the AI in the prompt creation. As mentioned before, prompt engineering is usually a process that is iterated in several versions until the optimal prompt for achieving the input is created. This process can be repeated many times and even be guided by techniques like zero-shot and few-shot prompting, as IBM explores in this article.

As important as the prompt itself is the AI model that reads it. How a model understands a prompt depends on its training, its capabilities, its limits, and the parameters that have been set for it.

What Makes a Good Prompt?

A good prompt needs to contain enough information to guide AI in completing a task but not so much that it becomes over instructed.

Clarity is key to prompt engineering, the AI must understand the prompt perfectly to deliver the right output.

Context of the task, which the model needs to understand the prompt. Background information for the prompt can be added to the context to help the model to better understand the prompt.

Constraints, output constraints (such as creating a table), audience, language, tone and also things that you want to avoid in the output.

Examples of desired output (the model learns to recognize a pattern in the examples provided). The prompt alone may not contain enough information to define certain patterns.

Good prompts for AI do more than ask a question and await an answer. The prompt must provide sufficient information for the model to perform the task at hand and to understand what results are expected.

What Are the Main Prompt Engineering Techniques?

In addition to zero-shot, one-shot, few-shot and multi-turn prompting, there are many other ways to steer a model towards completing a task using confirmations and other techniques for prompting.

Zero-Shot Prompting

For instance, a prompt to a model that asks it to sort out positive, negative and neutral content in customer reviews of a shop would have the model complete the task without any examples of expected output.

Simple prompt strategies can be useful when the task itself is not difficult and sufficient information for the task is provided in the prompt.

One-Shot Prompting

One-shot prompting: an example followed by a request to perform the same work. This allows the model to understand the relationship between input and output and therefore complete the request. One-shot prompting is useful when the desired format or style of the response is hard to explain in a prompt.

Few-Shot Prompting

Few-shot prompting includes several examples of input/data and the model learns from these and is then able to complete new tasks using the same patterns learned from the input/examples provided in the prompt.

For example, you can add examples to the prompt, like messages from customers that have been categorized properly by the model, and then use the same pattern in another message from customers. Few-shot prompting is particularly powerful when the task at hand has rules, or a specific format, that are not easily captured in a prompt.

Role Prompting

Note that having a role does not automatically give the model the knowledge to finish the task. Instead the role gives context on how to approach the task. This would be an example of an editor prompt, a data analyst prompt, a customer support assistant prompt, and a programming tutor prompt.

It’s important to note that assigning a role to a model does not necessarily mean that the model has the necessary knowledge to complete a task. Rather, the role of the model helps to guide the model on how to best complete a task.

Prompt Chaining

Prompt chaining refers to the process of completing a task by issuing a series of individual prompts. As opposed to describing a complex process in a single prompt, processes can be split up into individual prompts, each covering one step of the process.

Each step of the process can be controlled individually, and output from previous prompts can be used as input for subsequent ones.

Chain-of-Thought Prompting

Chain-of-thought prompting: This method supports the AI model in complex reasoning tasks by forcing it to work step by step to provide the final solution.

While forcing a model to display its thoughts is not very successful, it is possible to train a model to perform complex reasoning tasks by having it generate steps to arrive at a result. In particular, structured input, intermediate steps, and even a checking step can be used to arrive at the desired outcome.

Why Is Prompt Engineering Important?

As already stated, prompt engineering is important when working with Generative AI models. Most models do not know what a user wants with unclear input.

Understanding the individual words of a prompt does not necessarily imply understanding all of the aspects a user wants to have understood by them. In particular, understanding words of a prompt does not necessarily imply understanding an intended audience, a desired form or length, desired degree of detail, or desired purpose.

However, when a company uses AI to complete work, the work can be output in multiple formats, restrict usage to approved information and meet a multitude of quality guidelines.

Well-designed prompts help to reach the maximum output of current models and to reduce the amount of effort it takes to achieve a goal.

What Are the Benefits of Prompt Engineering?

Efficient interaction with Generative AI and relevant output are achieved by prompt engineering. A well-designed prompt clearly states the goals of a user interaction with Generative AI.

More importantly, a well-engineered prompt can ensure the model understands the task it is to complete, thus enabling the model to solve the task more effectively.

Using designed prompts also allows for consistency when using AI for tasks that are similar in nature (e.g. creating a long document, one section at a time).

Workflows for AI prompt design are a means to manage processes more efficiently and help users reach their goals more efficiently. You can design prompts that retrieve data from documents, sort requests, summarize longer texts, generate code, or even format data correctly.

For companies that are already working with a number of AI models, designing the prompt can also help them understand how these models generate output for similar input, and how to further improve them. Most prompts will need to be slightly modified for each model.

Where Is Prompt Engineering Used?

The practice of prompt engineering is used in many areas of generative AI.

Customer Service

Customer service systems – to help AI assistants handle customer questions and provide answers that match company rules.

The prompt for customer service AI would define the role of the AI assistant, the amount of information that can be used to answer a question, how the answer will look, and when human intervention is required.

Software Development

This can also be applied in Software Development: assist in writing code to complete a task, debug a program and describe the cause of errors, assist in searching for bugs, help writing documentation and even assist in finding a solution to a programming problem.

A program prompt typically includes a lot of background information about the task at hand, such as the programming language, framework or environment for which code is needed, and more. Moreover, a programmer might provide the code that he or she is currently working on, and error messages that the programmer encountered up until that point.

Healthcare

For organizing approved health information, summarizing health information, and creating tables from health information to name a few examples in the health space.

As prompt engineering is also applied to other tasks in healthcare, protection of corresponding tools has to be implemented. Also data protection, monitoring and human control have to be supported in prompt design.

Data Analysis

The analysis, summarization, and pattern discovery in your dataset. Additionally, AI can ask additional questions regarding the information analyzed and provide output in virtually any format.

However, when using AI to analyze business data, the model must have access to the same information as the user. A good prompt does not guarantee any results. The prompt has to be tested against several examples in order to verify how the model interprets the prompt and how it responds to slight variations of the prompt.

Content Creation

Writers & Marketing teams can use prompts to create blogs posts, summaries or even complete articles. You can also use existing content and re-phrase / re-structure it to reach a different audience.

This is particularly important when generating written content for people and therefore determining not only content but also overall style and how to organize it. Additionally, it is possible to specify words and phrases that must be used.

Cybersecurity

Prompt engineering is also becoming increasingly important for cybersecurity research and securing information. It can be used for analyzing security information, explaining security problems, automatically generating test cases for software, and more.

As with normal cybersecurity work, special attention must be given to potential security hazards when performing prompt engineering for cybersecurity related tasks. A particular attack to be aware of is prompt injection attacks, and other manipulations of input text to the AI.

What Are the Challenges of Prompt Engineering?

We want to stress that prompt engineering alone is not enough to guarantee results from AI. The quality of the resulting output greatly depends on the quality of the prompt, and, most importantly, on the quality of the model it interacts with.

Additionally, one would typically test the prompt on various examples to assess how the model interprets said prompt and generates output based on it, and, furthermore, how various slight modifications to the prompt change the generated output.

Over time, also models, applications and business needs change and therefore the prompts must be adjusted as well.

There is also the risk that the AI programs designed to process prompts could be threatened by attacks using prompt injection and other tactics to manipulate the model into returning responses of a certain nature. Thus, the design of prompts must also incorporate appropriate security measures.

Prompt Engineering and Fine-Tuning

When we talk about prompt engineering and fine-tuning we are talking about two different approaches to improve AI models for their users. While prompt engineering changes the input to a fixed model, fine-tuning a model changes the model itself and trains it on new data.

As opposed to fine-tuning, which typically requires suitable training data and a lot of compute, prompt engineering is typically easy to tweak and test.

Even if prompt engineering does not quite have the desired outcome, all is not lost. The same effect can also be reached with tuning, in combination with prompt engineering.