Prompt Engineering Examples To Supercharge Your AI Skills

Why Is Prompt Engineering Important?

Generative AI is probably one of the most promising elements in the future of digital marketing, as it helps us perform certain tasks faster. However, if you don’t know how to prompt engineer, you risk getting highly inaccurate and irrelevant outputs. Writing prompts can be tricky, and it takes a lot of trial and error before you can perfect your method. For starters, when you craft your prompt engineering examples, you should provide intent to the algorithm so it understands who you are and what your goal is. Don’t trust that it will format the output in your preferred way.

So, give specific directions on the format and length of the answer. At the same time, you can mitigate bias by simply asking AI to be inclusive. When you get your results, don’t trust them blindly but check to see whether they are accurate. Unfortunately, AI hallucinations are a real and quite common occurrence—during these occasions, AI basically comes up with misinformation. No matter how clear your AI prompt is, you should still bear in mind that the output may be partially fabricated.

But this shouldn’t scare you, because it’s part of the AI equation. A detailed prompt engineering guide can make your job easier and turn you into an AI master.

5 Different Types Of Prompt Engineering Examples

1. Zero-Shot

This is how almost everyone begins their ChatGPT prompt engineering journey. You simply ask a non-complicated question without setting any guidelines and wait until AI provides an answer. On this occasion, GenAI doesn’t need any training or examples to come up with an output. It uses its knowledge to craft answers using its unique way of language production. This is usually the first method developers use to test how accurately an AI tool works without any intent or specification.

2. One-Shot And Few-Shot

One-shot prompting uses a single example to describe to AI how it should perform a specific task. For example, if you want to come up with lead generation techniques for SaaS, you may offer another company’s successful tactics as a prototype. This one prompt engineering example helps AI understand its task better and lets it generalize using its pre-existing knowledge base. On the other hand, few-shot prompting uses 2–5 examples to train the algorithm. Therefore, AI can use this new knowledge and come up with the most relevant and reliable information. This method works best when you have to educate a pre-trained LLM using new sets of data.

3. Chain-Of-Thought

In the Chain-of-Thought (CoT) model, AI breaks down each task into steps. For example, it starts by identifying a problem, scavenging for useful tips, and finding the best solution. Thanks to specific ChatGPT parameters, AI keeps track of its reasoning and steps while providing outputs. Every step can be altered to ensure optimal results. However, once all steps are finalized, you can’t easily go back and make changes. This means that extra care is required when working with this model, but if done right, you can receive the most accurate outputs.

4. Prompt-Chaining

If you have a complex AI prompt, this method allows you to use multiple linked prompts that help the algorithm understand its intent better. Basically, each prompt output teaches something new to the next so it can refine itself and provide even more reliable information. This way, the model’s reasoning keeps improving, making it easier to handle complex tasks.

5. Self-Consistency

In this model, you can repeat the same question to see which answers will come up most of the time. Using the same prompt, AI usually comes up with varying outputs. While this may sound frustrating, think about how often different experts provide different viewpoints on the same matter. For instance, if you ask about the best content marketing ideas, you won’t get the same suggestions every time.

10 Prompt Engineering Examples All Marketers Should Try

1. Natural Language Processing Tasks

One of the most highly used prompt engineering examples is Natural Language Processing (NLP) techniques. Let’s start with smart assistants, like Siri and Alexa, that use voice recognition to understand a request and provide AI answers. Also, NLP has been used for years to enforce email filters by recognizing certain trigger words and classifying emails as spam. Not only that, but this model is applied every time we search for something on Google; the platform’s algorithm tries to understand and predict what we are trying to say. That’s why it shows a number of possible queries we may be looking for.

Another key benefit of the NLP model is translation. When you specify in detail what you intend to say, AI can produce accurate translations that will benefit your course localization projects. Marketing localization can also benefit you by personalizing your offers and boosting international SEO. Lastly, NLP databases can save phone call data for training purposes, so, based on the information we offer them, they understand human language and queries better.

2. Chatbots

This is undoubtedly one of the top applications of Artificial Intelligence in the world of marketing and customer service. Many businesses implement such tools on their websites to free up time from their reps’ schedules. When clients visit your domain and have a simple query, AI can provide relevant answers and prevent any more emails from jamming up your inbox. However, to make this prompt engineering example work successfully, you have to define your tactics and train the LLM model efficiently. For example, if you make alterations to your pricing models, you should let your chatbot know about it, as it may give out wrong information. Also, you must train it to speak conversationally and not like a soulless machine.

Once you figure out how to turn your chatbot into a useful virtual assistant, you can start using it to generate high-quality business leads. How? Well, it can ask each visitor for their contact details and what they are looking for. Once you gather this sort of information, you can include people in your segments and promote personalized content.

3. Content Creation

Some of the most popular AI prompts for marketing revolve around content. And this is understandable since content marketing for B2B is at the epicenter of most marketers’ radars. But how can you use AI in this area ethically? Let’s start with brainstorms, as you may use ChatGPT and other similar tools to come up with article ideas, subjects, and titles. You may also use the algorithm to perform better in your marketing efforts by generating effective ad copy and social media posts. However, don’t copy and paste AI’s outputs. You still need to work on the results to ensure the AI voice is humanized and the output matches your brand voice.

One of the top AI use cases for B2B marketing is content creation. Many marketers use generative tools to create entire articles and even eBooks. Yet, this process won’t teach you how to write better or improve your SEO performance. Not only that, but AI often plagiarizes material. You certainly don’t want to be accused or fined for using pre-existing content, do you?

4. Q&As

Not all prompt engineering examples are meant to produce groundbreaking material that transforms your life. Sometimes you just need to ask Gen AI simple questions and get concise answers. What does a prompt engineer do in this case? It is one of the least demanding tasks, as you only have to provide AI with relevant information so it can identify the scope of every question and locate the right information. Let’s say you ask AI about the newest marketing industry trends. The algorithm can probably produce endless text explaining the many trends in the current landscape. However, a prompt engineer should teach AI to be concise. This approach is especially useful for product companies that want to provide customers with clear answers regarding their products instead of letting them search through their FAQ pages.

5. Language Generation

The goal with prompt engineering examples is to drive accurate answers, but most easily accessible tools are trained to provide generic information, lacking scientific language. Many tools may struggle if they have to answer questions regarding medicine, physics, or technology. That’s why many prompt pros design and train their own tools that are proficient in a specific field. Google’s Med-PaLM is the perfect example, with the tech giant fine-tuning its AI tool so it has expertise in answering medical-related topics. You can do the same with your own tool. For example, if you are a marketer in the technology field, you may provide tech content marketing databases to AI so it can better understand your queries and goals.

6. Recommendations

Did you know that 61% of consumers are ready to spend more for personalized experiences? Still, it’s discouraging that only 25% of customer experiences are highly personalized. AI prompting can be your ally in your efforts to make shopping a unique experience for each client. You can offer advanced input systems with as much customer information as possible, including preferences, website visits, and past purchases. You can be ultra-specific by letting AI know exactly which products people bought in the past, prices, and unique features. This way, AI can recommend to each client products and services they may like. Additionally, it can deliver personalized offers based on their budget. Ask your smart tool to also write convincing text about why customers should buy this product and how it will help them solve their issues. This process can help companies offering content marketing services create even more targeted content.

7. Data Analysis

In the world of marketing, gathering and analyzing data and metrics is a never-ending task. That’s why you should leverage AI marketing benefits to receive unique insights. You can apply this prompt engineering example whether you want to build a new tool or fine-tune an existing platform. Once you gather your marketing results, feed them into your tool and ask it questions that help you make informed decisions. For instance, if your email data is disappointing, you can ask AI to provide you with reasoning and help you improve the situation. As an intriguing example, Google’s Med-PaLM has been trained to help doctors examine medical exams. How? Thanks to the expert database of the tool, doctors can ask specific questions and get questions pointing them in the right direction, saving them time.

8. Product Management

Software developers don’t always have a talented product manager who will create a foolproof roadmap and accelerate business growth. The first thing AI prompting can help you with in product management is creating user personas. Who would be interested in purchasing your solution? Instead of burying yourself in strategy books, you can ask ChatGPT to offer you general information about your industry audience. The more information you feed it, the more specific its output will be. So, you should do your homework before writing a prompt. Next, you can ask the algorithm to create a strategic roadmap that will help your SaaS B2B marketing efforts flourish. You should require key milestones, goals and outcomes, and solutions to potential roadblocks.

9. Project Management

Projects are often plagued by poor time management, resource wasting, and not meeting deadlines. AI prompting can help alleviate these major pain points as it can set deadlines and send timely reminders, allocate resources wisely, and time-track each process. Additionally, it can predict potential hurdles and offer solutions before you even need them. For example, even if you are using the best demand generation practices, leads may be slow to come. AI can predict reasons why this might happen and give you insights on how to overcome the slump. Lastly, AI tools boost team collaboration since they can summarize meetings and formal discussions into small paragraphs and give you an overview. Therefore, team communication is transparent since everyone knows what was discussed.

10. Sentiment Analysis

Finally, we reach the final prompt engineering example you can use to improve corporate functions. You probably receive many reviews, comments, and feedback from your clients depending on a multitude of things. Maybe they don’t like the price, would like an additional feature, or have trouble navigating your website. You have to gather all the feedback, analyze it, and understand the sentiment behind it. For instance, a relatively negative review may actually have a positive sentiment, as a customer’s goal may be to help you improve. Doing this analysis manually takes time, so it’s only natural to utilize AI to get tips. You may use a GenAI tool to detect people’s emotions, identify popular and unpopular product features, predict market trends, and understand people’s perception of you and your solutions. Isn’t it obvious, then, that prompt engineering should be among your digital marketing skills?

What You Should Know About ChatGPT Parameters

Temperature is the first aspect of the ChatGPT prompt engineering guide you should know about. Lying between 0.1 and 1.0, it refers to the randomness of a produced output. When you score 0.0–0.3, your output is more formal. On the other hand, when you score 0.7–1.0, your content is more diverse and creative.

The results here vary from 1 to 20+, indicating the number of relevant answers ChatGPT offers you. Fewer suggestions mean that your answer is highly focused, while more than 20 options deliver more creative content. Depending on the output you want to receive, you can indicate the desired number to the tool.

If you can’t decide on your top K, top P helps ChatGPT offer you the top options that account for a specific percentage of the total probability. For example, if you choose a 0.5 top P, the tool will give the options whose values account for 50% of the total probability. Such a parameter balances between focused and diverse content.

No matter what prompt engineering examples you use, ChatGPT limits how much information it processes at once. The lower the number of tokens, the shorter the answer is. So, if you want a longer, detailed answer, you may set the parameter to 100+ tokens.

There are two types of penalties: presence and frequency. The former refers to setting limits regarding the number of times the same word can appear within an output. The latter is similar but uses equivalent words to avoid repeating the same ones.

Key Takeaway

Generative AI is a gift to all marketers, as it helps you perform countless tasks, including a market intelligence report. Whether you are using zero-shot, prompt-chaining, or self-consistency type, you can direct AI to produce the output you expect. NLP tasks, chatbots, and content generation are three of the most popular prompt engineering examples, helping marketers create personalized and high-quality content and generate new leads. If you want to further leverage AI’s personalization features, you can use it to provide your customers with recommendations based on their preferences. Then, you can feed it with data so it can provide you with accurate insights.

At the same time, AI can handle even more complex processes, such as project management, product management, and sentiment analyses. The key to writing prompts is to know how to set parameters and give the algorithm as detailed information as possible.

If you want to receive more AI-related news, sign up for our newsletter for timely emails and the latest developments.