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**Most of Us Are Using ChatGPT Wrong**
Many of us aren’t leveraging ChatGPT to its full potential. Here are some common mistakes:
- **We don’t include examples in our prompts.**
- **We overlook the ability to control ChatGPT’s behavior by assigning it roles.**
- **We let ChatGPT guess instead of providing it with relevant information.**
These issues arise because we often rely on generic prompts that might work occasionally but fail to deliver consistent, high-quality results. To truly harness the power of ChatGPT, we need to master the art of **prompt engineering**.
In this guide, we’ll explore **4 key techniques** used in prompt engineering to help you get better, more reliable outcomes.
If you’re not a paid subscriber, you can read this article for free on my website.
This article is part of my newsletter, **Artificial Corner**, where I break down AI concepts in plain English. Join thousands of tech enthusiasts, programmers, and professionals who are learning to maximize the potential of AI. You can also grab my free cheat sheets there.
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### **Few-Shot Standard Prompts**
Few-shot standard prompts are an evolution of the standard prompts we’re familiar with, but with a critical addition: **examples of the task**.
Why include examples? Simple: **Examples significantly increase the likelihood of getting the desired result.** They provide the model with a clear understanding of the task and the expected output.
A few-shot standard prompt consists of three key components:
1. **Task Description**: A clear explanation of what needs to be done.
2. **Examples**: Demonstrations of the task being performed correctly.
3. **Prompt**: The beginning of a new example that the model should complete by generating the missing text.
By structuring your prompts this way, you guide ChatGPT to produce more accurate and relevant responses.
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