Lately, I've been diving into building AI-powered web applications to boost my own productivity. One thing has become crystal clear: the barrier to entry for building anything has dropped dramatically. In this AI age, anyone can create, learning new skills is faster than ever, and information is at our fingertips.
However, as we move forward, quality will be the ultimate differentiator. Half-baked products won't cut it. To truly stand out and retain users, quality is paramount. So, how can you level up your prompt engineering skills to consistently get high-quality responses from AI? Let's explore some key techniques to help you hit that sweet spot.
Use AI to Write Prompts (Yes, Really!)
Okay, this might sound a little meta, but hear me out: use AI to help you write better prompts. It might seem obvious, or maybe even a bit unnecessary, but it can seriously level up your results.
The clearer your prompts are, the better the AI will understand what you're asking for. Unless you're a professional writer (and let's be honest, most of us aren't!), using AI to refine or even completely rewrite your prompts can make a huge difference. Think of it as having an AI assistant dedicated to making your requests crystal clear.
For example:
Let's say you're trying to figure out how to add a progressive blur effect. You might start with a prompt like this:
how to add progressive blur
The problem with this prompt is that it's missing crucial context. Are you trying to add this effect in Figma? Framer? Or are you trying to implement it in a React project? What language are you using?
If you feed this vague prompt to an AI and ask it to improve it, you might get something like this:
I want to implement a progressive blur effect on images in my React project. Provide code examples and explanations for how to achieve this using CSS and JavaScript. Assume I am using a functional component approach and have basic knowledge of React hooks.
See how much more specific the refined prompt is? By providing details about the framework (React), the technologies (CSS and JavaScript), and your existing knowledge, you're much more likely to get a helpful and relevant response.
Zero Shot, One Shot & Few Shot Approach
When it comes to prompt engineering, you'll often hear the terms "zero-shot," "one-shot," and "few-shot." What's the deal with all these "shots"?
- Zero-Shot: This is when you ask the AI a question or give it a task without providing any examples. The AI's gotta rely on its own brainpower here.
- One-Shot: This is the middle ground where you give the AI one example to learn from. Think of it as a quick study guide.
- Few-Shot: This is when you do provide the AI with a few examples of the type of output you're looking for. This helps the AI understand the vibe you're going for.
It's often the case that you'll get better results with a few-shot approach, especially when you're trying to get the AI to generate content in a specific format.
Example:
Let's say you want the AI to write a social media post. With a zero-shot prompt, you might simply ask:
Write a tweet about the benefits of prompt engineering.
The AI will likely generate something, but it might not be very engaging or tailored to your specific needs.
With a few-shot prompt, you could provide an example like this:
Here are a few example tweets about prompt engineering:
- Prompt engineering is the secret weapon for unlocking the full potential of AI! 🚀 #PromptEngineering #AI
- Want better AI results? It all starts with better prompts. Learn the art of prompt engineering! 💡 #AI #PromptHacking
- Stop wasting time on bad AI outputs! Master prompt engineering and get the results you deserve. 💪 #ArtificialIntelligence
Now, write another tweet about the benefits of prompt engineering, following the same style and format.
By providing these examples, you're giving the AI a clear understanding of the desired tone, length, and use of hashtags.
The Tradeoff:
By giving the AI examples, you're also kind of putting it in a box. It'll see your examples and try to copy that style, which can limit its creativity. It's like saying, "Hey, be creative, but only this creative." So, keep that in mind! If you want something truly original, zero-shot might be the way to go.
A/B Testing
Okay, this might seem obvious, but it's important. If you're not A/B testing your prompts, you're leaving a ton of potential on the table.
Since there's no official "Prompt Engineering for Dummies" guide, A/B testing is how you figure out what works and what doesn't. Basically, you give the AI multiple prompts for the same desired result and see which one performs better.
Example:
Let's say you're trying to get the AI to write a compelling product description for a new noise-canceling headphone. You could try these two prompts:
- Prompt A: "Write a product description for noise-canceling headphones."
- Prompt B: "Write a persuasive and benefit-driven product description for 'Sony' noise-canceling headphones, highlighting their superior noise cancellation, comfortable design, and long battery life. Target busy professionals who work from home."
Run both prompts and compare the results. Prompt B is much more specific and provides context about the target audience and key features. You'll likely get a much better product description from Prompt B.
Measuring Your Results: How to Know What's Winning
So, you've run your A/B tests, but how do you actually know which prompt is better? Here are a few things to consider:
- Relevance: Does the output actually address the prompt? Does it stay on topic?
- Quality: Is the writing clear, concise, and grammatically correct?
- Engagement: Is the output engaging and interesting to read? Would it capture someone's attention?
- Usefulness: Is the output actually useful for its intended purpose? (e.g., does the product description effectively sell the product?)
- Subjectivity: Sometimes, it just comes down to your gut feeling. Which output do you personally like better?
Why This Matters (And Why We Don't Do It Enough):
We're so used to chatting with AI, giving it one prompt, then tweaking it with followup messages. But we almost never start a fresh chat with a completely different prompt. That's a mistake!
The best way I've found to really nail this is to use my own product (I built an AI tool). I keep using it, tweaking the prompts, and seeing what happens. It's a win-win because I'm not only improving my prompts, but I'm also making my product better at solving my own problems.
what fine tuning prompts does to a chat history 😅 — Samit Kapoor (@samitkapoorr)
Use XML tags
If you're not using them, then using XML tags in your prompts can be a game-changer maybe.
Think of XML tags as a way to give your prompts structure. And as we know, better structure = better understanding for the AI. And prompt engineering is all about hitting that sweet spot where the AI consistently gives amazing results.
How It Works:
XML tags allow you to clearly define different sections or elements within your prompt. This can help the AI understand the relationships between different pieces of information and generate more coherent and relevant responses.
Example:
Suppose you're building an AI wrapper that generates UI. You might start with a plain English description, but find it's not enough to enforce the desired behavior. Let's see how XML tags can level things up.
Version 0 (Plain English Description):
You are a helpful AI assistant that generates UI code. Be concise and use modern styling.
While this might give the AI a general idea, it's not very specific or enforceable.
Version 1 (Prompt with XML Tags):
<core_identity>
You are a UI code generation assistant called "UI-Gen", designed to create clean, modern UI components based on user requests.
</core_identity>
<general_guidelines>
- ALWAYS generate code that is compatible with React.
- ALWAYS use Tailwind CSS for styling.
- NEVER include comments in the generated code.
- ALWAYS be concise and efficient.
</general_guidelines>
By using XML tags, you're giving the AI a much more structured and enforceable set of instructions. You're clearly defining its core identity and providing specific guidelines for its behavior. This can lead to more consistent, predictable, and high-quality results.
Read other AI app's prompts
If you're obsessed with an AI application and you love the outputs it gives, try reading the prompts that are powering their application.
Now, of course, prompts are their super power, so they keep them a secret. But there's this github repo that you can checkout for yourself, it provides AI prompts to big names in AI like v0, cursor, lovable, cluely, cursor, etc.
Now, these may not provide you direct information on what to do to improve your prompts, but you can pick up a few insights just by reading through them. Apart from this, there are tons of open source AI projects, you can read their prompts as well.
Your Prompt Engineering Journey Starts Now
So, there you have it: Prompt Engineering 101. We've covered a bunch of stuff, from using AI to write better prompts to A/B testing and even sneaking a peek at other AI apps' prompts.
The key takeaway here is that prompt engineering is a skill, and like any skill, it takes practice. You gotta experiment, try new things, and see what works for you.
Hi, I’m Samit, a software engineer and freelancer passionate about building real world projects. If you’re looking to collaborate or just want to say hi, check out my portfolio. Let’s connect
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