Best Prompt Engineering Ultimate Guide 2023: Beginner to Advanced

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by Damir Yalalov

Published: May 14, 2023 at 8:38 am Updated: May 14, 2023 at 8:39 am

by Karolina Gaszcz

Edited and fact-checked: May 14, 2023 at 8:38 am

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Prompt engineering is becoming a popular subfield in AI and natural language processing, as researchers and developers benefit from prompt engineering skills to achieve impressive results from large language models (LLMs). Prompt engineering involves understanding and working with the underlying create specific types of input to yield targeted output. The Prompt Engineering Guide is a comprehensive online resource built to teach and support anyone looking to develop or sharpen their prompt engineering skills.

The Prompt Engineering Guide includes a comprehensive set of resources, including lectures and tutorials to help the learner gain a deep understanding of what Prompt Engineering is, how it can be used, and the different techniques to apply it. The guide covers topics such as preparation and design of prompting frameworks and data collections, post-collection analysis, inference, optimization, layer-level debugging, and more.

The guide also includes a section on creating “prompts”, a set of tools to assist the user in developing and deploying Promoting Frameworks, as well as tutorials on how to adjust and tune saved models with techniques such as transfer learning. This section covers important best practices for designing common, and complex prompts for tasks such as question answering and arithmetic reasoning, and demonstrates techniques to train and optimize models for particular tasks.

The Prompt Engineering Guide features a comprehensive set of reference materials and tools, such as the CMU artificial intelligence reading materials and benchmark scores, to help the learners quickly become productive.

This guide is designed to provide comprehensive, detailed understanding of Prompt Engineering, as well as the tools needed to put that understanding into action. Whether you are a new user, or an experienced practitioner, this guide will help you become an expert at Prompt Engineering.

Prompting Introduction

In order to effectively use language models (LMs) for a wide range of applications and research areas, prompt engineering is a relatively recent profession that focuses on creating and optimizing prompts. Quick engineering skills make it easier to comprehend the potential and constraints of large language models (LLMs). In order to increase LLMs’ performance on a variety of common and challenging activities, including question-answering and mathematical reasoning, researchers utilize quick engineering. Prompt engineering is a strategy used by developers to create reliable and efficient prompting methods that interface with LLMs and other tools.

This article goes over the fundamentals of typical prompts to give readers a general sense of how to engage with and educate large language models (LLMs) using prompts.

Basic Prompts

You can already accomplish a lot with prompts, but the quality of the outcomes is determined on how much information you supply. A prompt can include information such as the instruction or query you are providing to the model, as well as additional features such as inputs or examples.

Here’s an example of a basic prompt:

As you can see, the language model generates a series of strings that make sense in the context of “The sky is.” The result may be unexpected or unrelated to the task at hand.

This basic example also emphasizes the importance of providing more information or instructions on what we precisely want to achieve.

Let’s attempt to make it a little better:

Is that a better option? We told the model to complete the statement, therefore the outcome is much better because it does precisely what we requested it to do (“complete the sentence”). Prompt engineering is the process of designing appropriate prompts to direct the model to do a task.

The preceding example is a rudimentary illustration of what is now feasible with LLMs. LLMs today can do a wide range of complex tasks, from text summarization to mathematical reasoning to code development.

Parameter to Control LLM Response

You will communicate with the LLM directly or through an API when using prompts. A few factors can be configured to get various prompt results.

Temperature: Simply put, the lower the temperature, the more predictable the outcomes are in that the most likely next token is always chosen. Raising the temperature might encourage more diverse or innovative outputs by increasing randomness. The other potential tokens’ weights are essentially being increased. To encourage more factual and succinct responses in the context of fact-based QA, we might wish to utilize a lower temperature. It could be advantageous to raise the temperature for jobs like coming up with poems or other creative endeavors.

Top_p: In a similar vein, you can regulate how predictable the model is at producing a response by using top_p, a sampling technique with temperature called nucleus sampling. Keep this down low if you’re seeking for precise, factual responses. Increase to a greater figure if you want a wider range of answers.

In general, changing one rather than the other is advised. Remember that your findings may differ according on the LLM version you are using before moving on to some simple examples.

Standard Prompts

Few-shot prompting involves providing a small number of examples to a model to learn from, which can improve its performance on new tasks. The QA format version of few-shot prompts typically involves providing a question and a few possible answers for the model to choose from.

Few-shot prompts enable in-context learning, which allows language models to learn tasks based on only a few examples. More of this will be seen in action in the forthcoming guides.

Prompt Components

As we go over more examples and applications for prompt engineering, you’ll see that there are specific aspects that make up a prompt.

A prompt can include any of the following elements:

• Instruction – a specific task or instruction that you want the model to carry out.

• Context – external knowledge or additional context that can guide the model to better responses.

• Input Data – here is the input or query for which we are looking for a response.

• Output Indicator – indicates the output type or format.

A prompt does not require all of the components, and the format is determined by the work at hand. More concrete instances will be covered in future guides.

Prompt Engineering Tips

Why Design Prompt?

The most important details in this guide are that prompt design is an iterative process that requires experimentation to get optimal results. Using a simple playground like OpenAI’s or Cohere’s is a good starting point, and that you can start with simple prompts and keep adding more elements and context as you aim for better results. When designing a big task that involves many different subtasks, you can break down the task into simpler subtasks and keep building up as you get better results. The instructions can be designed by using commands to instruct the model what you want to achieve such as “Write”, “Classify”, “Summarize”, “Translate”, “Order”, etc. Experimenting with different instructions with different keywords, contexts, and data is important to see what works best for your particular use case and task. Instructions should be placed at the beginning of the prompt, and a clear separator like “###” should be used to separate the instruction and context.

Specific Instruction

The most important details in this text are that it is important to be specific about the instruction and task you want the model to perform, and that the more descriptive and detailed the prompt is, the better the results. It is also important to keep in mind the length of the prompt as there are limitations regarding how long this can be. Additionally, it is important to consider how specific and detailed the prompt should be, as too many unnecessary details are not necessarily a good approach. Experimentation and iteration are key to optimizing prompts for applications. An example of this is a simple prompt to extract specific information from a piece of text.

Avoid Impreciseness

The most important details in this text are that it is important to be specific and direct when creating prompts, as this is similar to effective communication. For example, when explaining the concept of prompt engineering, it is important to keep the explanation short, only a few sentences, and not be too descriptive.

Say What to Do

When designing prompts, it is important to avoid saying what not to do and instead say what to do instead. This encourages more specificity and focuses on the details that lead to good responses from the model. An example of this is a movie recommendation chatbot failing at exactly what the author wants it to do due to focusing on what not to do.

Basic Prompting

Summarization

Text summarization is one of the common jobs in natural language creation. Text summary comes in a wide variety of forms and applications. The capacity to quickly and simply summarize articles and concepts is one of language models’ most exciting potential uses. Let’s try a simple prompt-based summarizing exercise.

The model attempted to sum up the paragraph in one line without paying too much attention to the accuracy of the output above, which is something we shall discuss in a subsequent guide. The instructions can be used to your advantage, but we’ll save that for a later chapter. Please take a moment to explore to see if you can achieve better results.

Extraction

Language models are particularly competent of completing classification and a variety of other natural language processing (NLP) tasks, despite being taught to do natural language creation and associated tasks.

This is an illustration of a prompt that pulls data from a given text.

The above results can be enhanced in a variety of ways, but they are already quite helpful. It should be clear by this point that you can train the model to carry out a variety of tasks by giving it instructions. That is a potent potential that creators of AI products are already utilizing to create potent goods and experiences.

Question Answering

Improving the prompt format is one of the best strategies to encourage the model to respond to specific replies. As previously discussed, a prompt can integrate instructions, context, input, and output signs to produce better results. While these components are not needed, they are a good practice because the more explicit you are with guidance, the better the results. Here’s an example of how this may look after a more structured prompt.

Classification

So far, we’ve followed straightforward directions to complete a task. As a prompt engineer, you will need to improve your ability to provide better instructions. But wait, there’s more! You will also discover that for more difficult use cases, simply providing instructions will not suffice. This is when you should think more about the context and the various prompt pieces. Input data and examples are two other aspects you can supply.

Let us try to explain this using a text categorization example.

Conversation

One of the most intriguing things you can accomplish with prompt engineering is to train the LLM system on how to behave, its objective, and its identity. This is especially handy when developing conversational systems such as customer care chatbots.

For example, imagine a conversational system that can generate more technical and scientific responses to questions. Take note of how we directly tell it how to behave via the instruction. Role urging is another term for this.

Code Generation

Code generation is one application in which LLMs excel. Copilot is an excellent example. With ingenious suggestions, you can do a plethora of code-generation activities. Consider the following examples.

As you can see, we didn’t even need to identify the language. Let’s take it up a notch. I’d like to demonstrate how effective LLMs can be with a bit more work in designing the prompts.

Reasoning

One of the most difficult things for an LLM today may need some level of reasoning. Because of the types of complicated applications that can emerge from LLMs, reasoning is one of the areas in which I am most interested.

Some progress has been made in activities requiring mathematical aptitude. However, it is important to note that contemporary LLMs struggle with reasoning tasks, necessitating even more advanced prompt engineering techniques. These advanced strategies will be covered in the following handbook. For the time being, we shall examine a few fundamental examples to demonstrate arithmetic abilities.

Better instruction and examples can lead to more accurate results. This section of the guide will continue to incorporate more examples of common uses and discuss more complex prompt engineering principles in future guides to improve performance on more difficult tasks.

Advanced Prompting

Zero-Shot Prompting

LLMs today are able to do tasks in a single attempt because they have been trained on a lot of data and are adjusted to follow directions. In the part before, we experimented with a few zero-shot examples. One of the instances we gave is as follows:

Few-shot prompting is a technique that involves providing the model with a small number of examples or demonstrations to improve its performance in tasks where zero-shot capabilities are not enough. This approach can be particularly useful in scenarios where the model needs to learn new concepts quickly.

Few-Shot Prompting

Although large-language models now exhibit impressive zero-shot performance, they still fall short when applied to more challenging tasks. Few-shot prompting is a method for enabling in-context learning that helps to address this issue by guiding the model toward improved performance. For later situations when we want the model to respond, the demos act as conditioning.

Limitations of Few-shot Prompting

Although standard few-shot prompting is effective for many activities, it is still not a flawless method, particularly when handling more difficult thinking tasks. Now let’s show why this is the case. Do you remember the prior instance where we gave the task:

This is not the appropriate answer, which underlines both the shortcomings of current systems and the requirement for more sophisticated prompt engineering.

To check if adding certain instances improves the outcomes, few-shot prompting will be used. Few-shot prompting includes giving the system a little amount of training data that can aid in improving the system’s response accuracy. To evaluate if the system can provide better answers, we could, for instance, give it a few examples of appropriate responses to comparable queries.

Chain-of-Thought Prompting

Chain-of-thought (CoT) prompting, which was first described in Wei et al. (2022), permits complicated reasoning abilities through intermediary reasoning processes. On more difficult jobs that demand deliberation before replying, you can combine it with few-shot prompting to achieve greater outcomes.

Zero-Shot CoT

The concept of zero-shot CoT (Kojima et al. 2022), which essentially includes adding “Let’s think step by step” to the original prompt, is one that has recently gained popularity. Let’s test the model with a straightforward issue and see how it does:

It demonstrates the power of natural language processing and machine learning algorithms in understanding and generating human-like language. This approach can be applied to various tasks, such as chatbots, language translation, and text summarization.

Self-Consistency

Self-consistency is perhaps one of the more sophisticated prompt engineering strategies available. Self-consistency, as proposed by Wang et al. (2022), seeks “to replace the naive greedy decoding used in chain-of-thought prompting”. The goal is to sample a variety of different lines of reasoning using few-shot CoT and then utilize the generations to choose the most reliable response. This improves the effectiveness of CoT prompting on tasks requiring numerical computation and common sense thinking.

Let’s try the next arithmetic reasoning example:

The result is incorrect! How can we make this better by becoming self-reliant? Let’s give it a shot. The few-shot exemplars from Wang et al. 2022 will be used.

There are other processes involved in computing the final answer, but for the purpose of simplicity, we can see that a majority solution is already emerging, thus it would effectively become the final answer.

Generated Knowledge Prompting

The capacity to include knowledge or information to help the model produce more accurate predictions is a prevalent strategy for improving LLMs.

Can the model be used to produce knowledge before making a prediction, using a related concept? Create knowledge to be used as part of the prompt is what Liu et al. 2022 aim to do in their study. How useful is this in particular for things like common sense reasoning?

Let’s try this easy question:

With this example, some incredibly fascinating things transpired. The model was highly assured in the first response, but not so much in the second. For demonstration purposes, I simplify the procedure, but there are a few additional factors to take into account when coming to a decision. For more, see the paper.

Automatic Prompt Engineer (APE)

Automatic prompt engineer (APE) is a framework for automatic instruction creation and selection proposed by Zhou et al. in 2022. The issue of instruction production is presented as a black-box optimization problem involving natural language synthesis, and LLMs are used to generate and search through potential solutions.

A big language model is used in the first stage (as an inference model) to produce instruction candidates for a task by receiving output demonstrations. These potential answers will direct the search process. Using a target model to execute the instructions, the best instruction is then chosen based on computed evaluation scores.

The human-engineered “Let’s think step by step” prompt is outperformed by the zero-shot CoT prompt that APE finds (Kojima et al., 2022).

Prompt Implementation

Generating Data

LLMs are capable of producing text well. Effective prompting techniques can direct the model to provide better, more reliable, and factually accurate responses. LLMs can also be very helpful for gathering data that is necessary to conduct a variety of research. For instance, we can employ it to quickly produce the following examples for a sentiment classifier:

It’s quite helpful. In another section of the instructions, we apply this example to a different test. Here is a further concrete illustration of how to use the LLM for data labeling and augmentation tasks.

LLMs are versatile tools that allow users to perform a wide range of tasks, including programming, writing, solving mathematical problems, and creating music. They are ideal for individuals who need a single device that can handle multiple functions.

ChatGPT Prompt Engineering

The most recent prompt engineering techniques for ChatGPT are covered in this part, along with hints, applications, restrictions, papers, and extra reading material.

ChatGPT Introduction

ChatGPT is a new OpenAI-trained model that can converse with humans. This model has been trained to respond appropriately in the context of a discussion by following instructions in a prompt. ChatGPT may assist with answering inquiries, making recipe suggestions, writing lyrics in a specific style, creating code, and much more.

Reinforcement Learning from Human Feedback (RLHF) is used to train ChatGPT. While this model is far more competent than prior GPT versions (and has also been taught to lessen damaging and untruthful outputs), it is not without limitations. Let’s look at some specific examples of capabilities and constraints.

You can utilize the ChatGPT research preview here, but the examples below will use the Chat mode on the OpenAI Playground.

Multi-turn Conversations

To begin illustrating ChatGPT’s capabilities, we will employ the chatbot assistant example from earlier and discuss the outcomes. In contrast to text-davinci-003, the gpt-3.5-turbo model that drives ChatGPT accepts input in chat format. The AI model anticipates a series of messages and uses them to generate a response.

Single-turn tasks

The chat style supports single-turn jobs as we did with text-davinci-003, but it also supports multi-turn dialogues. This means that we can utilize ChatGPT to complete tasks that are comparable to those we have shown for the original GPT models. Let’s attempt to use ChatGPT to answer the following question as an example:

Adversarial Prompting

The study of adversarial prompting is crucial for understanding the dangers and security concerns associated with LLMs. The study of risk identification and technique design is crucial for solving problems.

The community has discovered a wide variety of hostile prompt assaults that include prompt injection of some kind. Below, we present a few of these instances.

When developing LLMs, it’s crucial to prevent against immediate attacks that could get past safety barriers and violate the model’s guiding principles. Below, we’ll discuss some instances of this.

Please be aware that some of the concerns mentioned below may have been addressed with the use of more reliable models. This implies that some of the prompt attacks listed below might no longer be as successful.

 It is important to regularly review and update models to ensure they align with current best practices and minimize the risk of violating guiding principles. Additionally, incorporating diverse perspectives and expertise in the development process can help identify potential barriers and improve the overall effectiveness of the model. 

Prompt Injection

By employing creative prompts that alter the model’s behavior, prompt injection seeks to take control of the model output. According to Simon Willison, these attacks “as a form of security exploit” could be dangerous.

Let’s go over a simple illustration to see how quick injection can be accomplished.

Remember that there is no set structure that the model anticipates when we create prompts; rather, we just chain instructions and all the various prompt components, including user inputs. Although this input flexibility is desirable, there is a risk that we may experience flaws like the prompt injection described above.

This specific attack appears to have been stopped, however you can experiment with more cunning prompts to see if the injection still works on the upgraded model.

Here is another straightforward illustration with many directives and tasks:

This attack works by injecting an instruction into the model output that, when executed, causes the model to ignore the original command and produce potentially destructive outputs.

Prompt Leaking

A type of prompt injection attack known as prompt leaking targets prompts that may include proprietary or confidential information but weren’t meant for the general public. Numerous startups are already creating and chaining carefully worded prompts that result in usable products constructed on top of LLMs. Developers should take into account the kind of rigorous testing that must be done to prevent prompt leakage because these prompts may contain sensitive IP that shouldn’t be made public.

Here is a straightforward illustration of prompt leaking:

The output mentioned above returns the examples, which may contain private information that you may use as part of the application prompt. Here, it is advised to be extremely cautious about what is passed in prompts and possibly use certain strategies (such optimizing prompts) to prevent leaks.

Prompt Jailbreaking

Some models will not comply with unethical requests, but they can be got around if the request is appropriately contextualized.

As an illustration, the following prompt was able to get around the content policy:

And there are numerous other ways to accomplish this to force the model to act contrary to its guiding principles.

Models like ChatGPT and Claude have been synchronized to prevent producing content that, for example, encourages unlawful or unethical activity. Since individuals are experimenting with these systems, we are learning new weaknesses even though it is tougher to jailbreak them.

Prompt Reliability

We’ve already seen how powerful well-designed prompts can be for a variety of tasks when employing methods like few-shot learning. It is vital to consider the dependability of these language models as we consider developing real-world applications on top of LLMs. This manual focuses on illustrating efficient prompting methods to raise the accuracy of LLMs like the GPT. Generalizability, calibration, biases, social biases, and factuality are a few issues of interest.

Prompt Factuality

LLMs have a propensity to produce responses that seem logical and compelling but are occasionally made up. The model can be improved to provide more accurate, factual responses and less inconsistent, made-up ones by improving the prompts.

Biases

LLMs have the potential to generate problematic generations that could be damaging and exhibit biases that could impair the model’s performance on subsequent challenges. Some of these can be reduced through clever prodding techniques, but others may call for more sophisticated solutions like moderation and filtration.

FAQs

Prompt engineering is an AI concept, specifically in natural language processing. Instead of being explicitly presented, the description of the task that the AI is expected to do is embedded in the input, for example, as a question. This allows the AI to understand the task and generate an appropriate response without needing explicit instructions. This concept has revolutionized many industries, including customer service and healthcare.

The Large Language Model is a models that is used to predict the next word in a sentence by using a large corpus of text.  These models have been shown to be highly effective in natural language processing tasks such as language translation, text summarization, and question answering. Additionally, they have the potential to revolutionize the field of artificial intelligence by enabling machines to understand and generate human-like language. 

Prompt injection is a way to exploit a user input prompt in order to execute malicious code. This can be done by entering code into the input prompt that is then executed by the server.  Injection attacks can have serious consequences, such as stealing sensitive data or taking control of the affected system. Therefore, it is important to implement proper input validation and sanitization measures to prevent such attacks. 

A prompt leak is when a website or application discloses too much information in its error messages or prompts. This can give attackers clues about how to exploit the system or exfiltrate data.

Prompt jailbreaking is a type of jailbreak that allows you to gain access to the system of your LLM. This gives you the ability to modify the system and make changes to the overall model. Jailbreaking can make it more vulnerable to security threats. It is important to weigh the pros and cons before deciding to jailbreak your LLM. 

Read more related articles:

• Best 10 AI Prompt Guides and Tutorials for Text-to-Image Models: Midjourney, Stable Diffusion, Dall-E
• Microsoft and KPMG Join Forces to Bring Generative AI Solutions to Professional Services
• Top 50 Text-to-Image Prompts for AI Art Generators Midjourney and DALL-E
• Midjourney intends to employ prompt engineers to enhance its AI model

About The Author

Damir is the team leader, product manager, and editor at Metaverse Post, covering topics such as AI/ML, AGI, LLMs, Metaverse, and Web3-related fields. His articles attract a massive audience of over a million users every month. He appears to be an expert with 10 years of experience in SEO and digital marketing. Damir has been mentioned in Mashable, Wired, Cointelegraph, The New Yorker, Inside.com, Entrepreneur, BeInCrypto, and other publications. He travels between the UAE, Turkey, Russia, and the CIS as a digital nomad. Damir earned a bachelor's degree in physics, which he believes has given him the critical thinking skills needed to be successful in the ever-changing landscape of the internet. 

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Damir Yalalov

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Damir Yalalov

Damir is the team leader, product manager, and editor at Metaverse Post, covering topics such as AI/ML, AGI, LLMs, Metaverse, and Web3-related fields. His articles attract a massive audience of over a million users every month. He appears to be an expert with 10 years of experience in SEO and digital marketing. Damir has been mentioned in Mashable, Wired, Cointelegraph, The New Yorker, Inside.com, Entrepreneur, BeInCrypto, and other publications. He travels between the UAE, Turkey, Russia, and the CIS as a digital nomad. Damir earned a bachelor's degree in physics, which he believes has given him the critical thinking skills needed to be successful in the ever-changing landscape of the internet. 

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