What is “Prompt Engineering”? If you have been using ChatGPT or other AI tools for a while, you will inevitably come across this term. Prompt engineering is often the solution, especially if you are searching for ways to improve your interaction with AI and the quality of its responses.
Prompt engineering involves a set of techniques used to craft precise and effective inputs (which are what we call “prompts”) for AI systems to generate the desired output. The main purpose is to optimize your commands or instructions to the AI, so that the responses from it are relevant, accurate, and useful. By understanding and applying the principles of prompt engineering, you can significantly enhance your interaction with AI tools and reap the best outcomes possible.
Table Of Content
- Why is Prompt Engineering Important?
- 1. Improved Accuracy
- 2. Efficiency and Clarity
- 3. Error Reduction
- Good Prompt vs Bad Prompt
- Key Components of Prompt Engineering
- Clarity and Specificity
- Role-Playing and Persona
- Providing Context and Examples
- Iterative Refinement
- Other Best Practices for Effective Prompt Engineering
- Applying Prompt Frameworks
- Setting Constraints
- Chain of Thoughts
- Prompt Chunking
- Checking for ‘Hallucination’
- Conclusion
Why is Prompt Engineering Important?
At first, you might feel that applying prompt engineering techniques to deliberately craft your prompts slows you down. However, as you become more familiar with them, you’ll realize this often reduces the overall time and effort needed to get the AI to complete tasks for you effectively, and it shouldn’t be mistaken as an additional workload.
In addition, there are three main reasons why prompt engineering is important:
1. Improved Accuracy
The adage in the Computer Science world, “garbage in, garbage out”, applies to your interaction with ChatGPT too. A common beginner’s mistake is issuing prompts that are too general without providing much specifics for ChatGPT to work on. Users who do this can expect responses that are generic and often not useful from ChatGPT. This can be fixed by masterfully engineering your prompts.
2. Efficiency and Clarity
Do you find yourself going through rounds of revision with ChatGPT before it finally gets your task done right (or before you give up)? Prompt engineering helps you craft your prompts to reduce the need for multiple iterations. As you will see later in this article, clear and specific inputs enable ChatGPT to quickly grasp the context, thereby making your interactions more efficient and productive.
3. Error Reduction
By providing detailed and unambiguous prompts, you minimize the chances of errors in ChatGPT’s responses. And do you know that when nonsensical outputs are received from ChatGPT, it’s not always the user’s fault? Look out for tips later in this article on how you can detect and correct errors in ChatGPT’s responses.
Good Prompt vs Bad Prompt
Before we go further, let us illustrate what a good prompt looks like compared to a bad one. The example below will give you a clearer picture.
Here’s a bad prompt:
Tell me about cats.
This prompt is too vague and broad. What exactly is someone expecting from ChatGPT with this prompt? ChatGPT won’t know for sure. It might provide a very general response covering random facts about cats, which may not be useful for his specific needs. You might get an overview of cats, their behaviors, or even myths about cats, but it won’t be focused or detailed.
Compare that with this good prompt:
Provide an overview of the different breeds of domestic cats, focusing on their characteristics and care requirements.
This prompt is clear and specific. It instructs ChatGPT to focus on domestic cat breeds and narrows down the requirement by asking it to focus on details about the cats’ characteristics and care. With a prompt like this, ChatGPT’s response will likely be a detailed and organized one – closely matching what the user expects.
See the difference? The question then is, how do you craft a good prompt? We have the answer below.
Key Components of Prompt Engineering
There are certain characteristics that most well-engineered prompts exhibit. Here are four key components you will find in the most effective prompts:
Clarity and Specificity
These are among the most important characteristics of an effective prompt. Clarity means the prompt is easy to understand, logically structured, and free of ambiguity; Specificity involves providing detailed and precise instructions, and being narrowly focused on your query.
The example in the “Good Prompt vs Bad Prompt” section has demonstrated what an unclear and unspecified prompt looks like and the opposite, so we won’t repeat it here.
Role-Playing and Persona
A well-engineered prompt often assigns a role or persona to ChatGPT. This will help it understand the context of the query and provide direction, so that ChatGPT can tailor its responses more specifically to the needs of the query.
For instance, you could prompt:
You are an expert online marketer with 10 years of experience in social media marketing. Explain the benefits of social media marketing to a small business owner.
Providing Context and Examples
We have highlighted throughout this article the importance of providing context and a clear direction to ChatGPT. One of the effective ways to do that is by including relevant background or “contextual” information in your prompt.
For example, a History teacher’s prompt with contextual information might look like this:
I am a high school history teacher preparing a lesson for my students on the Industrial Revolution. My students are familiar with basic European history, but this is their first in-depth look at the Industrial Revolution. The lesson should cover the causes, key events, and impacts of this period on society and economy.
See how this helps ChatGPT grasp the scope the teacher is expecting?
Another common prompt engineering technique is providing examples. Relevant examples add a reference on top of your clear instructions to ChatGPT. This is especially helpful when you are struggling to construct a thorough prompt – just show ChatGPT what you want with examples!
Iterative Refinement
Prompt engineering is an iterative process. This means you should expect going to and forth with ChatGPT for a few rounds before it produces the most ideal outputs – especially if your task is a complex one.
Refine your prompts based on the responses you receive. If the initial response isn’t quite right, adjust your prompt and try again. Giving examples and references, as mentioned above, will help.
Have fun doing so.
Other Best Practices for Effective Prompt Engineering
Here are a few more tips to complete our discussion on crafting effective prompts:
Applying Prompt Frameworks
Instead of starting from scratch every time you construct a prompt, why not follow existing prompt frameworks that are proven to work?
A prompt framework helps to ensure you include all the necessary elements for a successful interaction with ChatGPT. Examples of these frameworks include:
• RACE or RICE: Role, Action/Instructions, Context, Examples
• CARE: Context, Action, Results, Examples
• ROSES: Role, Objective, Scenario, Expected Outcomes, Steps
• TAG: Task, Action, Goal
Notice that each of these prompt frameworks embodies the four key components of prompt engineering—clarity, specificity, role-playing, and context. We will discuss prompt engineering frameworks in detail in another article.
Setting Constraints
Setting specific constraints or conditions for the response defines the ‘rules’ and scope for ChatGPT’s outputs. For example, do you need a 500-word or 1000-word article? Should it be written in layman’s terms or a professional tone? What biases or sensitive topics should ChatGPT avoid?
By letting ChatGPT know how far it can (or cannot) go, you help it tailor its output to precisely meet your requirements.
Chain of Thoughts
ChatGPT’s outputs can sometimes feel like ‘black boxes’— you don’t get to peep at the steps it has taken to arrive at those responses.
There are times when ChatGPT’s responses look suspicious. Or when it involves a complicated process, and you want to make sure ChatGPT has done it right. This is when the “Chain of Thoughts” technique is useful.
How do you do that? Simple. Just add the magic phrase “Let’s do it step-by-step” or “Let’s go step-by-step” at the end of your prompt, and this will trigger ChatGPT to lay out its reasoning process for you.
For example, when we issue this prompt:
What is 2 – 2. Let’s do it step-by-step.
If we hadn’t included the magic phrase in our prompt, ChatGPT would have simply answered “2 minus 2 equals 0”. But because we did, ChatGPT broke down the steps for us:
Of course, this is a trivial example. You can use the same technique when you get into more serious discussions with the AI. Additionally, you can use this to get ChatGPT to explain its answer if it doesn’t seem right to you too.
Prompt Chunking
You don’t need to squeeze everything into a single prompt. When your subject cannot be explained in a few sentences, instead of sending a lengthy, winding, and overly complex prompt, you can break it down into smaller, more manageable prompts and issue them to ChatGPT sequentially. This approach makes it easier for ChatGPT to understand your task and respond accurately.
Checking for ‘Hallucination’
In the context of AI, “hallucination” refers to instances when ChatGPT generates information that is incorrect, misleading, or entirely fabricated, even though it may sound plausible.
This is a limitation known to happen not only in ChatGPT but also in other AI tools, which is why we cannot place 100% trust in AI yet.
Asking ChatGPT “Are you hallucinating?” when you encounter such responses is not enough to counter this limitation. For one, the AI does not have the self-awareness or ability to assess its own answers. The due diligence is on the user to review and fact-check ChatGPT’s answers. Good old Google often is still our best friend here.
Conclusion
In this article, we’ve looked at prompt engineering, the practice of crafting precise and effective instructions for ChatGPT. It’s important to engineer our prompts because doing so leads to accurate, relevant, and useful outcomes from ChatGPT.
We covered 4 key components of prompt engineering: Clarity and Specificity, Role-playing and Persona, Providing Context and Examples, and Iterative Refinement. Additionally, a number of best practices to enhance your experience and improve your productivity with ChatGPT were revealed.
Do you have other prompt engineering tips you want to share with us? Drop them in the comments.
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