Ultimate Guide to Prompt Engineering

How to Write Prompts and Master Generative AI

In this article, we’ll cover everything you need to know about prompt engineering and provide practical examples to help you confidently use AI to assist you. By developing strong prompting skills, you’ll be able to tap into the full potential of these AI tools and get the most out of them.

We’ll answer questions like:

  • What is prompt engineering, and why is it important?
  • How can you write effective prompts to get the best results from AI?
  • What are some practical examples of well-crafted prompts?
  • What techniques can you use to optimize your prompts for better outputs?

By the end of this article, you’ll have a strong understanding of prompt engineering and be well-equipped to start using AI to assist you in various tasks.

Note: this is the first in a series of guides on Prompt Engineering, in future articles, we will cover Limitations of LLMs, Prompting Techniques, How to Evaluate and Optimize Prompts, Walkthrough of Different LLM Settings and How to Prepare Data for LLMs.

What is prompt engineering

Prompt engineering is the process of writing prompts to get the best possible results when interacting with generative AI models like ChatGPT, Claude, or Gemini. It’s about communicating clearly so that the AI understands what you want it to do and has the right context to generate high-quality, relevant outputs that meet your needs

While the term “prompt engineering” might sound complex, it’s not as difficult as it’s made out to be. It’s a combination of art and science that requires you to think carefully about how you phrase your prompts to elicit the desired response from the AI.

Why are prompts important?

Prompts are the main way to interact with and guide AI language models like ChatGPT, Gemini and Claude. Although prompt engineering is a relatively new field, prompting is becoming an increasingly important skill.

Think of prompts as your instructions or commands to AI – they specify what you want the AI to do. This could be answering a question, writing an article on a certain topic, summarizing a piece of text, analyzing the sentiment of some comments, or any number of other language tasks AI is capable of. The prompt is where you give the AI its assignment.

Mastering the skill of writing effective prompts allows you to interact with and leverage the capabilities of these large language models (LLMs) and get AI to do exactly what you want.

How should you talk to AI language models?

Think of AI language models like extremely intelligent but very literal assistants. If you give clear, specific instructions and provide good background context, you’ll likely get the result you’re looking for. But if your instructions are vague, the output may be irrelevant or nonsensical. Remember, garbage in, garbage out.

While LLMs are powerful, they have limitations and can make mistakes, or even produce biased or inconsistent outputs at times. Don’t blindly trust everything an LLM says – think critically and use your own judgment.

Approach LLMs as a tool to enhance your own intelligence and creativity, not as a replacement for human thought. You provide the direction, domain knowledge, and creative spark, while the LLM acts as a powerful aid to help you get there faster.

Finally, be prepared to experiment and iterate. If a prompt doesn’t give you quite the result you want, refine it and try again. Over time you’ll develop an intuition for crafting effective prompts.

How can you interact with an AI LLM?

There are several ways you can interact with an AI language model. Most are accessible through websites like ChatGPT, Claude or Gemini. You can type in a prompt or question and receive a response from the AI from your browser. They provide a simple and user-friendly way to have back-and-forth conversations with the model.

But if you want to do something more complex, or if you know how to work with APIs, you can also use an API to send prompts and receive responses, allowing you to integrate the LLM into your own applications and tools.

However, with PromptDrive.ai, you can collaborate on ChatGPT, Claude and Gemini prompts and workflows from one easy to use dashboard. An additional benefit is that you’ll be able to take advantage of AI for a fraction of the cost because you only pay for what you use. You do need to add your own API keys, but the process to get one is straightforward and we have guides to help you with that.

How should you structure a prompt?

A great prompt is one that effectively communicates your intent to the AI language model and guides it to generate the desired output. One of the easiest ways to achieve this is by following a clear prompt structure, most prompts will contain four key parts.

  1. Task
  2. Context
  3. Input Data
  4. Output Indicator

We’ll go into more detail about each of these parts and it’s important to remember that you won’t always need to include all four parts for a prompt.

Instructions

The instruction section is a critical component of a well-structured prompt. You need to provide clear and specific instructions in your prompt as this helps the AI understand exactly what you want it to do.

When your instructions are clear, the AI is less likely to get confused or generate irrelevant responses so be as specific as possible about the task you want the AI to perform.

In most cases, your instructions will contain action verbs like “write,” “summarize,” “compare,” “analyze,” or “generate”.

Context

The context section is where you provide any relevant background information, constraints, examples, or context the model needs to complete the task accurately. You should try to provide relevant context as this helps the AI better understand the task at hand and generate more accurate responses.

Context could include:

  1. Relevant facts, figures, or data points
  2. Definitions of key terms or concepts
  3. Historical information or timeline of events
  4. Descriptions of entities, objects, or systems
  5. Excerpts from articles, reports, or other sources

Aim to include enough information to help the model understand the situation without overwhelming it with unnecessary details. Keep in mind that the context section should be concise and focused on the most important aspects related to the task.

Remember, it’s important to provide context that is accurate, unbiased, and relevant to the question or instruction. Misleading, false, or irrelevant context can lead the model to generate responses that are off-topic, inconsistent, or factually incorrect.

Input Data

This is where you provide the specific data or information that you want the AI to process or analyze to generate a response. Your input data could be text, questions, examples, data points, or any other information the AI needs to work with.

It’s important to prepare and clean your data beforehand to ensure you get accurate responses. If you’re providing structured data like tables, lists, key-value pairs, etc., use a clear, parsable format like JSON, CSV, XML, or markdown.

You need to include enough data for the AI to effectively carry out the task you’ve specified in the instructions and specify the type of data being provided if it’s not obvious from the formatting.

Output Indicator

The output indicator helps guide the model to provide an output that matches your needs and desired format. Your output indicator could consist of one or more of the following elements:

  1. Output format: Specify the format you want the output in, such as a numbered list, bullet points, paragraphs, a table, JSON, CSV, etc. This helps the model structure its response accordingly.
  2. Scope and detail: Indicate the level of detail and scope you expect in the output. Do you want a high-level overview or an in-depth analysis? Specifying this helps the model gauge how much information to include.
  3. Key information: If there are certain key points, topics, or information that must be included in the output, mention them in the output indicator. This ensures the model covers the essential aspects you require.
  4. Output length: If you’re restricted to a certain number of words or characters, specify an approximate word count or number of sentences. This prevents the model from generating overly long or short responses.

While describing your output indicator is useful,  including an example of your desired output format within the prompt can be highly effective. This gives the model a clear template to follow and understand your expectations better.

Prompt examples: good vs bad

To illustrate the importance of prompt engineering, let’s take a look at some examples of good and bad prompts across four common use cases: answering questions, writing articles, summarizing text, and analyzing sentiment. These examples will demonstrate how you should ask AI to complete tasks for you, and although the “good” prompts are effective, there’s always room for improvement.

Prompt to answer a question

Good prompt:

“What are the key differences between the American Revolution and the French Revolution in terms of their causes, goals, and outcomes?”

Bad prompt:

“Tell me about revolutions.”

Prompt to write an article on a certain topic

Good prompt:

“Write a well-structured article discussing the environmental and economic benefits and challenges of widespread adoption of electric vehicles. Include relevant statistics, expert opinions, and real-world examples.”

Bad prompt:

“Write about electric cars.”

Prompt to summarize a piece of text

Good prompt:

“Please summarize the main points and conclusions of the following research paper, focusing on the methodology, results, and implications for future studies in the field. Keep the summary under 300 words.”

Bad prompt:

“Sum up this text.”

Prompt to analyze the sentiment of comments

Good prompt:

“Analyze the sentiment (positive, negative, or neutral) of the following customer reviews for a recently launched smartphone. Identify key aspects that contribute to the sentiment and provide a brief summary of the overall sentiment distribution.”

Bad prompt:

“What do people think about this phone based on these comments?”

As you can see, the “good” prompts provide more context, specific instructions, and desired outcomes, which help the AI generate more relevant and useful outputs. However, these prompts can still be refined further by including additional details, such as the target audience for the article or the specific aspects to focus on when analyzing sentiment.

Finally, you’re almost always going to need to improve your prompt. Especially for complex tasks and requests, that’s where iterating prompts can help you.

How do you improve prompts?

Prompt engineering is an iterative process and you probably won’t write the perfect prompt on your first attempt. Instead, you need to analyze the outputs you’re getting, identify areas for improvement, make tweaks to your prompt, and test again. When you are iterating on your prompts, keep these rules in mind:

  1. Keep a version history: Each time you modify your prompt, save the new version so you can track the changes you’ve made and revert back if needed
  2. Compare with other prompts: Research prompts others have used for similar applications to get inspiration 
  3. Know when to stop: Prompt engineering has diminishing returns. Once you have a prompt delivering good results, resist the urge to endlessly tweak it, use it and move on.

We’ll cover evaluating and optimizing prompts in more detail in a future article, but the key is to start with clear objectives, make changes methodically, and use more advanced prompt engineering techniques if needed. That said, one of the quickest ways to get better responses from AI is to add prompt modifiers.

What are prompt modifiers?

Prompt modifiers are additional instructions included in your prompt that alter the behavior, personality, knowledge, or output of the AI language model. They work by giving the model a specific “frame” or “role” to operate within when responding to your prompt.

They allow you to get more creative, specific, expert-level responses by giving the AI detailed instructions on how to behave or what perspective to take. Some common examples of prompt modifiers include:

Role-playing modifier

Role-playing modifiers instruct the AI to take on a specific role or persona while responding to the prompt. This modifier is helpful when you need the AI to provide responses from a particular perspective or with a specific expertise.

Example prompt:

“Act as an experienced journalist and write an article about the impact of climate change on coastal cities.”

To use this modifier, start your prompt with “Act as a {{role}}” followed by your specific request.

Writing style modifier

Writing style modifiers specify the desired writing style for the AI’s response, such as persuasive, informative, or descriptive. This modifier is useful when you require the AI to generate content in a specific tone or style to suit your needs.

Example prompt:

“Write a persuasive paragraph encouraging people to adopt a plant-based diet.”

To use this modifier, include the desired writing style in your prompt, such as “Write a {{writing style}} piece about {{topic}}.”

Output format modifier

Output format modifiers define the format of the AI’s response, such as a list, table, or step-by-step guide. This modifier helps structure the AI’s response in a way that is easy to read and understand.

Example prompt:

“Create a table comparing the features and prices of three popular smartphone models.”

To use this modifier, specify the desired output format in your prompt, such as “Generate a {{format}} of {{content}}.”

Length modifier

Length modifiers specify the desired length of the AI’s response, such as a specific word count or a range. This modifier is helpful when you need the AI to provide concise or detailed responses based on your requirements.

Example prompt:

“In 50 words or less, summarize the main theme of the novel ‘To Kill a Mockingbird.'”

To use this modifier, include the desired length in your prompt, such as “In {{word count}}, explain {{topic}}” or “Provide a {{length}} explanation of {{topic}}.”

Perspective modifier

Perspective modifiers instruct the AI to respond from a specific point of view or opinion. This modifier is useful when you want to generate content that aligns with a particular viewpoint or explores multiple perspectives on a topic.

Example prompt:

“From the perspective of a small business owner, discuss the potential impact of raising the minimum wage.”

To use this modifier, start your prompt with “From the perspective of {{viewpoint}}” followed by your specific request.

Target audience modifier

Target audience modifiers specify the intended audience for the AI’s response, such as a specific age group or level of expertise. This modifier helps ensure that the AI’s response is appropriate and tailored to the intended audience.

Example prompt:

“Explain the concept of photosynthesis in simple terms suitable for a 5th-grade science student.”

To use this modifier, include the target audience in your prompt, such as “Explain {{topic}} in a way that is suitable for {{audience}}.”

Emotion or tone modifier

Emotion or tone modifiers instruct the AI to respond with a specific emotion or tone, such as optimistic, sarcastic, or concerned. This modifier is helpful when you want the AI’s response to convey a particular feeling or attitude.

Example prompt:

“Write a sarcastic response to the following statement: ‘I love waiting in long lines at the DMV.'”

To use this modifier, include the desired emotion or tone in your prompt, such as “Respond to {{statement}} with a {{emotion/tone}} tone.”

Final word

You should now have a solid understanding of the techniques and best practices for writing effective prompts. You’ve learned the importance of providing clear instructions, context, data and output indicators to help AI models generate accurate and coherent responses.

Remember, prompt engineering is an iterative process that requires experimentation. With the knowledge and skills you’ve gained from this guide, you are now equipped to get AI to complete a wide range of tasks. As you grow more comfortable, challenge AI to complete increasingly complex tasks. Push the boundaries of what you thought was possible, and you’ll be amazed at the results you can achieve.

However, don’t forget to take the time to review and fact-check the outputs, ensuring that the information is accurate, relevant, and aligns with your intended goals. Remember, AI is a tool to enhance and augment your own knowledge and skills, not a replacement for human judgment and expertise.

Finally, prompt engineering is a rapidly evolving field, with new techniques and approaches constantly emerging as AI language models become more sophisticated. Staying up-to-date with the latest research and experimenting with different prompting strategies will help you get the most out of these tools.

FAQs about prompt engineering

Do I need to be an expert in prompt engineering to use AI effectively?

No, you can get started and achieve great results with the basics of prompt engineering. Focus first on writing clear instructions and providing sufficient context. As you gain experience, you can layer on more advanced techniques as needed to further optimize performance.

Do I need to be a programmer to learn prompt engineering?

No, you don’t need programming skills for prompt engineering. It’s more about understanding how to communicate effectively with AI using natural language.

Are there any prerequisites or technical requirements?

The prerequisites and technical requirements for prompt engineering depend on the specific AI tool or platform you’re using. Some AI chatbots and user interfaces are accessible via web browsers and require no special setup. Others may require creating an account, obtaining API keys, or installing specific libraries or dependencies. Always check the documentation or guidelines provided by the AI tool you’re using.

Can prompt engineering be used with any AI chatbot or language model? 

The core concepts of prompt engineering apply across AI language models. However, the specific capabilities, limitations, and formatting requirements may vary between different models. It’s important to understand the characteristics of the AI model you’re working with to create effective prompts.

How long does it take to get good at prompt engineering?

Prompt engineering skills can be improved over time with practice and experience. While you can learn the basics quite quickly, and if you’ve made it this far — you know enough to get started, most people get better at optimizing their prompts through trial and error and seeing what works well for different applications.

Do I have to write prompts in a specific format or syntax? 

No, prompts can be written in natural language – you don’t need any special formatting or syntax. The key is to be clear and specific in your instructions.

Can I just ask the AI simple questions or does it have to be a “prompt”?

Asking the AI straightforward questions is totally fine – a question is a type of prompt! Even simple queries like “What is the capital of France?” are prompts that specify the task you want the AI to perform (answering the question). That said, for more complex tasks, it helps to provide more detailed prompting with step-by-step instructions and relevant context.

How much can I trust the outputs of an LLM?

While LLMs are remarkably capable, they have certain limitations. Use your judgment, fact-check important claims, and think of them as an aid rather than an oracle.

What should I do if the model’s response is incorrect or not what I wanted?  

First, check if there are ways to clarify or improve your prompt to better communicate what you’re looking for. If the model makes a factual error, you can try prompting it to double-check its facts or provide a correction. If the response is completely irrelevant, there may have been an issue with the input or settings – try rephrasing and resubmitting.

How many iterations does it usually take to perfect a prompt?

The number of iterations varies widely depending on the complexity of the task, but it’s common to go through at least 3-5 rounds of testing and refinement, if not more. Don’t get discouraged if your early attempts aren’t perfect.

How long should a prompt be?

Prompts should generally be as concise as possible while still providing sufficient context and instructions. Aim to keep the total tokens (prompt + response) under the model’s token or context limits.

How detailed should a prompt be?

A prompt should be detailed enough to convey your intent and provide the necessary context for the AI to generate the desired output. However, it should not be so detailed that it becomes confusing or overwhelming. Strike a balance between providing sufficient information and keeping the prompt concise and easy to understand.

How long should the context section be?

The length of the context section can vary depending on the complexity of the task. Aim to provide enough information for the model to understand the background without including unnecessary details.

How specific should the output indicator be?

The output indicator should be as specific as possible while still allowing some flexibility for the model. Provide clear guidelines, but avoid being overly rigid, as the model may not always be able to follow extremely specific instructions perfectly.

What happens if I don’t include an output indicator?

If you don’t include an output indicator, the model will still generate a response based on your instruction and context. However, the format and scope of the output may not align with your expectations. Including an output indicator helps ensure you get the type of output you need.

Can I combine multiple prompt modifiers in one prompt?

Yes, you can stack modifiers to further specify the model’s behavior, like “Act as an experienced couples therapist and speak in a calm, empathetic tone.” Just be sure the modifiers don’t contradict each other.

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