Artificial Intelligence (AI) has taken over the world in the past year or so, and chances are, you have heard of (or experimented with) generative AI tools such as ChatGPT. But are you truly maximizing the marketing potential of generative AI?
If you’re ready to leverage generative AI and gain a deeper understanding of how it works, this ultimate guide will help you master both the art and science of prompt engineering to take your efforts to the next level!
What is Generative AI and Prompt Engineering?
First, let’s begin by defining generative AI. Simply put, generative AI is any algorithm that can create new content, be it text, image, video, or audio via machine learning (ML).
Prompt engineering is a way of structuring sentences and requests to guide AI systems, specifically large language models (LLMs) like ChatGPT, to provide more targeted and relevant responses.
It involves crafting effective prompts (i.e. input queries) to help AI models produce the desired output by minimizing any potential misinterpretations and maximizing the accuracy of the generated output.
3 Common Types of AI Prompts
Explore three common types of prompt engineering techniques:
1. Zero-shot Prompting
Zero-shot prompting refers to situations where you provide a single input to the model and expect it to understand and execute the task without having previously provided any similar examples. Under these conditions, you’re hoping the model generalizes from all of its prior training to sufficiently handle the request.
2. Few-shot Prompting
On the contrary, few-shot prompting is providing the model with a few examples of the task at hand as part of the input. The idea here is to guide the model to the format and nature of the output you desire.
In the example below, you can see we are asking ChatGPT to respond to (fictitious) customer inquiries. We start by providing two examples of existing inquiries and responses we find appropriate. Then, we ask it to mimic those responses for the new inquiry.
Few-shot Prompting Example
Help me respond to customer inquiries, respond in a similar manner to the two examples below:
Example 1:
Customer Inquiry: “I have not received my order yet, what’s going on?”
Response: “Dear Customer, we’re sorry to hear about your experience. Our team is looking into your order status and we will provide you with an update shortly.”
Example 2:
Customer Inquiry: “The product I received is damaged. Can I get a refund?” Response: “Dear Customer, we apologize for the inconvenience. We’re processing your refund request and will update you soon.”
New Inquiry: “I want to change my shipping address. How can I do that?”
Dear Customer, thank you for reaching out to us. We understand that you would like to change your shipping address. We’re here to assist you with that. Please provide us with your order details, including the order number, so we can locate your shipment in our system.
Once we have the necessary information, we will do our best to accommodate your request and update your shipping address. Thank you for your patience, and we will be in touch with you shortly with further instructions.
3. Chain of Thought (CoT) Prompting
Chain-of-thought prompting, also known as “dialogue prompting” or “prompt chaining”, involves breaking down a complex task into a series of simpler tasks. This method relies on maintaining a continuous dialogue with the model where each question builds on the last response.
Let’s look at an example where we want to generate a comprehensive marketing plan for a new product.
This is a complex task that can be broken down into several steps, like target audience identification, channel selection, message creation, and so forth.
CoT prompting example
Here’s an example of how a chain-of-thought prompting session could go (responses truncated for brevity):
User: “Describe our new product, a lightweight, waterproof, hiking boot made from recycled materials.”
LLM: “Our new product is a high-performance hiking boot, designed with the adventurer in mind. Crafted from…”
User: “Based on that description, who would be our primary target audience?”
LLM: “The primary target audience for this product would be outdoor enthusiasts who enjoy hiking, trekking, or other adventurous activities. They are likely…”
User: “What would be the best marketing channels to reach this target audience?”
LLM: “To reach this audience, consider utilizing outdoor adventure blogs, eco-friendly lifestyle websites, and social media platforms where outdoor enthusiasts…”
User: “Can you draft a promotional message for this product targeted at our audience, to be used on Instagram?”
LLM: “Embark on your next adventure with confidence and care for the planet! 🌍 Our new hiking boots…”
As you can see, each step builds on the previous response, allowing the user to gradually extract more complex and detailed information from the LLM.
In marketing use cases, this is often the most powerful form of prompting, with recent studies showing that CoT prompting can improve the reasoning ability of an AI model by forcing it to “show its work”.
3 Reasons why Marketers Need Prompt Engineering
By now, you might be thinking, “I’m a marketer. Why do I need to use AI prompting?” To answer that question, here are a few of the primary reasons marketers are embracing prompt engineering.
1. Time and Cost Savings
Prompt engineering can save valuable time and resources for marketing teams by allowing generative AI to create initial draft content more efficiently. With appropriate prompts, AI models can quickly generate copy (for assets like ads, promo videos, and blogs), answer customer inquiries, and create plan outlines, which frees up your team to focus on strategy and creative ideas.
Prompt engineering is typically used in the ideation/brainstorming phase by helping you quickly generate new ideas. While the content won’t necessarily be perfect, it can still provide valuable inspiration in a variety of different tones and styles, which can lay the foundation for your own creativity and unique content.
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2. Improved Marketing Campaigns and More Engaging Content
Effectively engineered prompts can improve marketing campaigns and create engaging content that speaks to your target audience. A well-crafted prompt ensures AI models generate content that aligns with your brand voice, resonates with your audience, and drives the desired action, which can ultimately lead to increased conversions.
Another hidden benefit of AI prompt engineering is that the act of writing out a prompt forces you to articulate what you want, which can not only give you a better understanding of what you’re looking for, but will also improve your ability to explain it when you’re collaborating with other, real-life humans. Chain-of-thought prompting is particularly good for this task, as you’re able to adapt and refine your prompts depending on the outputs, which helps translate your prompts from abstract ideas into something more specific and actionable.
3. It’s A Must-Have Skill for Marketers
As AI plays an increasingly prominent role in marketing, mastering prompt engineering is likely going to become a vital skill for professionals in the field. The World Economic Forum predicts that 97 million new jobs will emerge by 2025 due to humans and machines working together, and prompt engineering is one of those roles. This makes prompt engineering a critical and teachable skill for marketing professionals.
And while ‘prompt engineering’ may sound like something futuristic or intimidating, it’s really not all that different from what marketers have already been doing for decades with their creative briefs and multiple rounds of feedback sessions. But the biggest difference here is the audience – instead of speaking with a human, you’re interacting with a model based on a large dataset that can adapt its tone and style on the fly.
5 Ways To Master AI Prompt Engineering
Mastering AI prompt engineering requires a mix of creativity and precision to unlock the full potential of AI tools. To get the best results, follow these expert tips that will help you craft clearer, more effective prompts.
1. Start by Understanding the Technology
If you want to extract the most value out of AI, it’s important to thoroughly research and understand the capabilities of the underlying AI model you are using. While you don’t need a master’s in computer science or an in-depth background in machine learning, being familiar with the basics of AI and how LLM’s function will help you use these tools more effectively.
One of the most popular examples is ChatGPT by OpenAI. If you’re using this kind of AI platform, it’s essential to be aware of specific factors that can influence its performance, such as:
- Limitations and bias: A large language model only knows what it’s been trained on, which is true for any AI model. So even when a large dataset is being used, there are limitations on the accuracy and relevancy of the information it provides based on timeframes (i.e. ChatGPT4 currently has limited knowledge of anything that occurred after December 2023). OpenAI has been very candid about the biased nature of its LLM and it’s important to keep that in mind with any AI model – especially if you’re using it to generate content. Once again, just because a large dataset was used, it still only knows the data it was trained on.
- Hallucinations: A LLM like ChatGPT can throw together a response that reads and sounds accurate, yet actually contains false information. Other times the response may be very clearly false or even nonsensical at times. In either case, this is referred to as a ‘hallucination’ and anyone using generative AI for anything other than experimentation or play should be aware of this. It’s imperative to verify the accuracy of any output provided by a generative AI model.
- Context Window: The context window refers to the maximum number of input tokens or words that an AI model can understand within a given request. Be mindful of your tool’s context window capacity to ensure the relevant information is not left out and that the model processes your instructions effectively. This may translate to having to segment your prompts into a sequence of requests in order to properly inform the model of your desired output.
- Token Limits: Token limits dictate the maximum number of tokens or words an AI model can generate as a response. Understanding the token limits of your model will help you shape your prompts accordingly, enabling you to manage the length and complexity of the generated content. Much like the context window, you may need to break your requested outputs into sections in order to achieve the desired results.
- Model Parameters (if using the API): If you’re working with AI platforms through their APIs, it’s important to understand the available model parameters. These parameters can enable you to adjust settings such as temperature and max tokens, allowing you to fine-tune the AI model’s behavior and generate desired outputs.
If you invest the time to gain a solid understanding of generative AI and its capabilities, you’ll be better equipped to craft prompts that deliver the results you desire.
2. Practice Writing More Effective Prompts
Writing effective prompts requires a blend of creativity, precision, and a solid understanding of your AI platform’s capabilities.
Here are some essential tips to remember:
- Set a clear goal: Make your prompts well-defined, unambiguous, and clearly state the role or persona. In the case of ChatGPT, we can specify a role and area of expertise to set the tone and expectations for the AI’s response. For example, “As an SEO expert, provide advice on optimizing website content for search engines.”
- Break down complex tasks into simpler subtasks: Divide a complex task into smaller, more manageable parts to help the model better understand your needs. For example, instead of asking for a full marketing plan, request specific elements, like “list three key components of an effective SEO strategy.”
- Provide context: Offer relevant context or background information related to the role or task. For example, “Our company specializes in eco-friendly products for environmentally conscious consumers.”
- State the task or question: Clearly state the task or question you want the model to address. For example, “Please explain how we can improve our website’s presence on search engines”
- Set constraints or limitations: Mention any constraints or limitations that should be considered while generating the response. For example, mention constraints, like “Suggest strategies that do not require a large budget.”
- Offer additional guidance: Provide further instructions or examples to guide the model towards the desired output. For example, give further instructions, such as “Focus on on-page SEO techniques like optimizing title tags and meta descriptions.”
- Specify format, tone, and style: Mention the desired format, structure, or tone in your prompt. For example, “Provide the information in a concise, actionable list with a friendly tone.”
- Use double quotes: Enclose specific phrases or instructions in double quotes to emphasize their importance. For example, “Include best practices for on-page SEO optimization.”
When you put these all together, it gives us a prompt that looks like something like this:
“As an SEO expert, provide advice on optimizing website content for search engines. List three key components of an effective SEO strategy for our company. As a company we specialize in eco-friendly products for environmentally conscious consumers. Please explain how we can improve our website’s presence on search engines. Suggest strategies that do not require a large budget. Focus on on-page SEO techniques like optimizing title tags and meta descriptions. Provide the information in a concise, actionable list with a friendly tone. Include best practices for on-page SEO optimization.”
By following these tips, you can get more out of the AI model, enhancing your marketing campaigns and making your communication efforts more efficient and impactful. Remember, like any skill, prompt engineering improves with practice, so don’t be afraid to experiment and learn as you go.
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3. Use Prompt Engineering Tools
The explosion in popularity of OpenAI’s ChatGPT model has led to the emergence of numerous third-party apps aimed at enhancing and simplifying prompt engineering for end users to help avoid pitfalls. They also provide additional resources, like methods for organizing and managing prompts, that go a long way in assisting individuals in designing effective prompts for AI models.
Agenta
Agenta is an advanced prompt engineering tool designed to enhance AI workflows, offering seamless integration with ChatGPT and other language models. Agenta enables users to quickly generate various forms of content such as blog posts, email campaigns, and creative copy. The platform caters to industries like marketing, customer support, and more by streamlining the creation of tailored prompts for specific use cases.
With a vast library of pre-built templates, Agenta makes it easy for users to customize outputs, optimize writing tone, and maintain brand consistency. The platform supports both free and premium features, including advanced tools for creating complex workflows, providing a flexible solution for individuals and businesses looking to leverage AI more effectively.
AIPRM
AIPRM is a marketplace that offers 1-click prompts for ChatGPT, Midjourney, and DALL-E. It provides a quick and easy way to generate marketing content, such as blog titles, original articles, and copy for service pages.
The platform has over 121,000 prompt engineers and also has a browser extension with curated prompt templates for SEO, SaaS, and more. The extension requires an AIPRM account and offers free and premium features, including custom lists, custom writing tones, and custom writing styles.
AI Prompt Genius
AI Prompt Genius’ free browser extension takes things a step further, making it easier than ever to generate high-quality text from AI models. By providing locally synced chat history for convenient searching and offering a multitude of pre-designed prompt templates, AI Prompt Genius substantially simplifies the prompt engineering process for users.
While all of these tools aim to make prompt engineering easier, it is ultimately up to you as the user to feed it the best information possible to receive high-quality results.
4. Human Oversight is Still Needed
With any AI model, whether it’s used for translation, transcription, or facial recognition, it’s crucial to maintain human oversight throughout the process. In other words, don’t take any LLM’s output at face value. Whatever piece of content you are creating should be reviewed by yourself or someone else just as you would working with your human counter-parts. AI is here to make our lives easier, not replace us (or so they say).
5. Avoid Using Confidential Information
It’s important to remember that any data input into a public LLM like ChatGPT is not secure. This means you should avoid putting in any proprietary, client, or otherwise sensitive information as data security nor confidentiality are guaranteed.
Top 5 Prompt Engineering Examples of for Marketers
For a more practical understanding of the power and versatility of prompt engineering, let’s explore a variety of common marketing use cases. These examples cover a mix of common marketing activities:
1. Blog Post Topic Ideas
Prompt:
Generate {Number} unique and captivating blog post topic ideas in the field of your choice based on my company name and product description. The topics should be specific and attention-grabbing, providing enough material to write a full blog post, and is SEO friendly.
My company name is {Company Name}.
The product description is {Product Description}.
The target audience is {Target Audience}.
2. Homepage Title and Meta Descriptions for SEO
Prompt:
Act as an SEO expert tasked with optimizing the homepage of a company/product for search engines. Your client has provided you with the company/product name, a brief product description, and a primary keyword. Your job is to create a compelling title and meta description for the homepage that will both accurately reflect the content and appeal to potential visitors. Remember to keep in mind the target audience and the search engine algorithms when crafting your title and meta description. Provide {Number} results.
{Company/Product Name}
{Product Description}
{List of Keywords}
3. Press Release Title & Introduction
Prompt:
Acting as a News Editor, I will create a compelling press release title and introduction for your news story. Please provide me with the details of your press release, including the subject, company or product name, and a relevant keyword. Provide {Number} results.
{What is your Press Release about?}
{Company/Product Name}
{Keywords to Include}
4. LinkedIn Connection Request
Prompt:
Using the best practices for LinkedIn connection requests, such as personalization, brevity, clarity on mutual benefits, and a distinct call to action, please generate a connection request. Use the following details:
- {Prospect’s Name}
- {How We’re Connected}
- {Clear Reason for Connecting}
- {Desired Action}
Remember the total length of the output message should be ideally 140 characters or less.
5. Google Ads Writer (two-part)
For this example, we will use chain-of-thought prompting by first asking the LLM to tell us specifications and best practices for what we are trying to achieve–in this case, Google Ads.
Prompt 1:
List out Google Ads specifications for text ads, and concise best practices.
Prompt 2:
Using the given guidelines above for creating text ads on Google Ads, please design a compelling ad for {Company Name}.
The ad should promote {Product Name}, which is best described as {Product Description}.
Ensure that the ad includes relevant keywords, a strong call-to-action, and mention if we have any offers or discounts. Use concise language and ensure that the content is both appealing and relevant to our landing page. Please keep in mind the character limits for the headlines, display path, and descriptions.
The ad should look like:
Headline 1: ____
Headline 2: ____
Headline 3: ____
Display Path 1: ____
Display Path 2: ____
Description 1: ____
Description 2: ____
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