AI Prompt Engineering: How to Write Prompts to Get Desired Results?
AI is everywhere, but it doesn’t always give you what you want. That’s where prompt engineering comes in—learning how to ask clearly and specifically so AI understands you and delivers results that actually match your ideas.
Artificial intelligence is being used for everything nowadays, from getting search results to building apps. However, there’s one problem: You type in a prompt, hit enter, but the response doesn’t match your expectations.
It’s a skill to get AI to work for you. That’s where “AI prompt engineering” becomes relevant. It helps you craft prompts that an AI model can understand. Thus, giving you the results that you’re looking for.
In this prompt engineering guide, we’ll cover how you can tweak your prompts to get AI to draw your expected results. We’ll also learn about AI bias and how you can avoid it using prompt engineering.
What is AI Prompt Engineering?
AI prompt engineering is the art of giving clear instructions to AI so that it understands exactly what you’re looking for. In simple words, you learn to write a prompt that is easily understandable for an AI model.
The key to good AI prompts is to be as clear about your task as possible. Provide context and be specific about the way you want your output to be. For example, a prompt such as “Summarize this book for me” will get you a vague summary in paragraphs.
Instead, you can rewrite the prompt as, “Summarize this book in five bullet points and one takeaway” to get a much clearer and well-formatted summary. It’s simply because the second prompt provided context on what you wanted.
4 Types of AI Prompts
LLMs are trained on massive databases. Thus, the way you phrase your prompts highly impacts the outputs you get. Here are 4 major types of AI prompts that you can learn from:
1. Zero-Shot Prompts
Zero-shot prompts are short instructions that you give to an AI model. Such prompts include no examples and are mostly used for common tasks.
An example of zero-shot prompts can be: “Translate the word ‘Hello’ to French.” It’s clear, simple, and to the point. However, such vague instructions also lead to less controlled and inconsistent outputs.
2. Few-Shot Prompts
Few-shot prompts are more detailed prompts that include a few examples. Since these prompts include examples, they allow the AI to create similar results.
Here’s an example: “Write a LinkedIn post for me on [topic]. It should be in the same format and style as the following posts: [Post 1], [Post 2].” Few-shot prompts are typically helpful in generating ‘similar’ responses.
3. One-Shot Prompts
One-shot prompts are the middle-ground between zero-shot and few-shot prompts. These prompts include at least one example. However, they aren’t as descriptive as few-shot prompts.
Here’s an example: “Take reference from this [image] and generate a duckling for me in the same style.” You can use one-shot prompts for quick yet specific outputs.
4. Detailed Prompts
Detailed prompts, as the name suggests, include details on the style, formatting, and output you want. Such prompts are helpful when you want the AI to provide a specific result.
Let’s take an example: “Create a mood tracking app for me. The color scheme should have muted, pastel tones. Moreover, the app should include features such as mood tracking, journal notes, and reminders for the day.”
Detailed prompts are helpful when you need a specific output, such as a mobile app with a certain style and features. This way, you can get the desired results without creating a chain of prompts.
AI Prompt Engineering Tips for No-Code Developers
Apart from different types of prompts, you can use the 4S prompt writing formula to write good prompts. The 4S stands for: Simplicity, Specificity, Sensitivity, and Structure.
1. Simplicity
Ensure that your prompt is clear, simple, and to the point. Instead of leaving room for any guesswork, provide clear instructions to the AI. This will lead to a desired output.
2. Specificity
Be specific with the details of your task. It could include the tone, color scheme, format, word limit, and other such details. Add every detail to your prompt, making it clear and specific.
3. Sensitivity
To make the prompt even clearer, you can create a persona for the AI. For instance, you could ask the AI to act like an outreach expert and write a cold email for your client. You can provide context about the task to get closer to the outcome.
4. Structure
The last of the 4S’s suggests structuring your prompt well. In simple words, the prompt should have a clear, logical flow. Thus, making it easy for the AI to understand your requirements.
Iterative Prompt Engineering
Iterative prompt engineering is another way of prompt writing. It includes a chain of prompts that are adjusted to get closer to the desired result. With this approach, you can experiment and refine your prompts until you get your output.
How to Write Prompts to Build AI Mobile Apps in Natively?
If you can write good prompts, you can actually build a functional AI mobile app in Natively. This is how it works:
1. You need to Sign Up for a paid tier first to start using the platform.
2. Once done, you can start writing your prompt. Ensure that your prompt clearly describes the app you want to build, such as:
3. Click “Enter” and let the AI build for you. This is what our app looks like:
The app has everything that we asked for it to include—even the weekly calorie tracker. You can build any sort of app with Natively, such as trackers, games, or assistants.
AI Bias: How to Avoid It With AI Prompt Engineering?
AI models are trained on existing human data and thus, they’re prone to biases. This can lead to inaccurate and unreliable results. Hence, it’s crucial to guide the AI with your prompt to get accurate outputs.
Let’s learn about AI bias and how you can avoid it.
What is AI Bias?
AI bias is the inaccurate results LLMs produce when they’re trained on incomplete or prejudiced data. You can find examples of AI bias on all scales. For instance, an AI might assume that in your sentence, “The nurse walked into the room”, the nurse is a female because of the training data.
Sometimes, this can lead to real-life consequences as well. For example, an AI might rate white patients as a higher priority than Black patients in a hospital setting because it’s trained on the historical bias.
Such biases mostly occur because the data used to train LLMs reflect human biases. It could also happen due to incomplete data sets. In any case, it’s important to address them to avoid any bad outcomes.
How to Correct AI Bias with Prompt Engineering?
AI cannot make judgment calls. However, you can correct its biases with strategic prompt engineering. It works as you become specific and detailed about what you want the AI model to do.
For example, if you prompt the AI to “build a meditation app”, it is likely to create an app with a feminine touch because that’s the data it’s trained on. If you instead write, “build a meditation app for men that is personalized for their workout sessions too”, the AI is likely to create a neutral app.
You can consider the following best practices for prompt engineering:
- Be specific: Include every detail in your prompt that could allow AI to understand your thought process better. Avoid leaving any room for assumptions.
- Make the AI ‘think’: You can make the AI find more information on your prompt if you tell it to “not create a generic output”.
Conclusion
AI prompt engineering can be tricky, especially if you’re looking for something specific. That’s why you might need to refine your prompts repeatedly until you get the desired results. Iterative prompt engineering is possible for AI tools such as Gemini. In fact, it can get you great results.
At the same time, you must know that each AI works differently. It may take a few tweaks or none, depending on how well you’ve crafted your prompt and your expected outcome. Prompt engineering is a skill that can only be refined.