So, you’re back for more prompt engineering ideas? Awesome, let’s see what we can learn today!

If you’ve ever felt like arguing with ChatGPT because it just didn’t get what you meant, congratulations! You’ve met the joys of prompting and I’m hopefully going to help you stop that from happening today.

As a software engineer, I’ve seen how AI models can be fine-tuned through techniques like agents, RAG, chain-of-thought prompting, zero-shot prompting, and few-shot prompting. But for now, since we’re dealing with ChatGPT, Claude or other AI and not writing code, let’s focus on one of my absolute favorites, few-shot prompting. Because honestly, getting AI to do what you actually want it to do shouldn’t be a painful experience.

The way you structure your input prompt can have a huge impact on the quality of the output response you receive back from whatever AI tool you are using. That’s where prompt refining techniques like few-shot prompting come in.

Few-shot prompting is a technique that can help AI Chatbots generate more logical and more useful answers, making your responses and interactions with AI feel less like a guessing game and more like you know what you’re doing. Ok, Misty, but what exactly is few-shot prompting? So glad you asked…

Few-Shot Prompting: Guiding AI with Examples

Few-shot prompting is exactly what it sounds like. Instead of just asking a question and hoping for the best, you show AI what you want first by providing a few examples. AI picks up on the format, tone, and expected output so instead of guessing at what you want, it actually follows your lead.

Why Few-Shot Prompting Works

One of the things that I love most about AI is that it can learn. Think of your first day on a new job. You didn’t know what to do, until you were shown. But once you were shown, you could repeat it over and over again consistently. AI is capable of this same thing.

Without Few-Shot Prompting:

With Few-Shot Prompting:

In full transparency, this is a very simple example, and as you can see in the “without few-shot” example, ChatGPT can give me a reasonable response back that the sentiment was negative, however, this is more than just a content issue. This could also be a format issue. If I don’t give it any reference points, it’s left to guess what “positive” or “negative” even means, and it doesn’t know if I want a full description or a simple single word. But if I show it a few structured examples first, AI gets the hint and suddenly, it’s no longer making guesses.

When It’s Best to Use Few-Shot Prompting

• When you want the responses to follow a specific format (Example: Structuring code comments so they always match company guidelines.)
• When you are refining because the responses you received were too vague or inconsistent (Example: Summarizing customer reviews into one-sentence takeaways because you want an overall consensus.)
• When you are training ChatGPT to perform a new or modified task (Example: Having ChatGPT output responses in JSON format so you can feed them into a database.)

Generally speaking, few-shot prompting gives the AI you are using a point of reference, making its answers more reliable and tailored to your specific needs.

Make AI Work For You

Few-shot prompting isn’t just some neat trick. It’s an amazingly easy strategy to master so that you can finally get AI to give you the responses you want, rather than giving you back nonsense.

So the next time AI gives you an answer that makes you roll your eyes, don’t just accept it…train it. Show it a few examples, refine your prompts, and get the kind of responses that make your life easier. Because let’s be honest, AI Chatbots may be powerful, but they still need a little coaching to get things right. Kind of like me before my morning coffee. 🙂