Examples of Prompts
In the previous section, we introduced and gave a basic examples of how to prompt LLMs.
In this section, we will provide more examples of how prompts are used to achieve different tasks and introduce key concepts along the way. Often, the best way to learn concepts is by going through examples. Below we cover a few examples of how well-crafted prompts can be used to perform different types of tasks.
Topics:
Text Summarization
One of the standard tasks in natural language generation is text summarization. Text summarization can include many different flavors and domains. In fact, one of the most promising applications of language models is the ability to summarize articles and concepts into quick and easy-to-read summaries. Let's try a basic summarization task using prompts.
Let's say I am interested to learn about antibiotics, I could try a prompt like this:
Prompt:
Output:
The "A:" is an explicit prompt format that's used in question answering. I used it here to tell the model that there is an expected further. In this example, it's not clear how this is useful vs not using it but we will leave it that for later examples. Let's just assume that this is too much information and want to summarize it further. In fact, we can instruct the model to summarize into one sentence like so:
Prompt:
Output:
Without paying too much attention to the accuracy of the output above, which is something we will touch on in a later guide, the model tried to summarize the paragraph in one sentence. You can get clever with the instructions but we will leave that for a later chapter. Feel free to pause here and experiment to see if you get better results.
Information Extraction
While language models are trained to perform natural language generation and related tasks, it's also very capable of performing classification and a range of other natural language processing (NLP) tasks.
Here is an example of a prompt that extracts information from a given paragraph.
Prompt:
Output:
There are many ways we can improve the results above, but this is already very useful.
By now it should be obvious that you can ask the model to perform different tasks by simply instructing it what to do. That's a powerful capability that AI product developers are already using to build powerful products and experiences.
Paragraph source: ChatGPT: five priorities for research (opens in a new tab)
Question Answering
One of the best ways to get the model to respond to specific answers is to improve the format of the prompt. As covered before, a prompt could combine instructions, context, input, and output indicators to get improved results. While these components are not required, it becomes a good practice as the more specific you are with instruction, the better results you will get. Below is an example of how this would look following a more structured prompt.
Prompt:
Output:
Context obtained from Nature (opens in a new tab).
Text Classification
So far, we have used simple instructions to perform a task. As a prompt engineer, you will need to get better at providing better instructions. But that's not all! You will also find that for harder use cases, just providing instructions won't be enough. This is where you need to think more about the context and the different elements you can use in a prompt. Other elements you can provide are input data or examples.
Let's try to demonstrate this by providing an example of text classification.
Prompt:
Output:
We gave the instruction to classify the text and the model responded with 'Neutral' which is correct. Nothing is wrong with this but let's say that what we really need is for the model to give the label in the exact format we want. So instead of Neutral we want it to return neutral. How do we achieve this? There are different ways to do this. We care about specificity here, so the more information we can provide the prompt the better results. We can try providing examples to specify the correct behavior. Let's try again:
Prompt:
Output:
Perfect! This time the model returned neutral which is the specific label I was looking for. It seems that the example provided in the prompt helped the model to be specific in its output.
To highlight why sometimes being specific is important, check out the example below and spot the problem:
Prompt:
Output:
What is the problem here? As a hint, the made up nutral label is completely ignored by the model. Instead, the model outputs Neutral as it has some bias towards that label. But let's assume that what we really want is nutral. How would you fix this? Maybe you can try adding descriptions to the labels or add more examples to the prompt? If you are not sure, we will discuss a few ideas in the upcoming sections.
Conversation
Perhaps one of the more interesting things you can achieve with prompt engineering is instructing the LLM system on how to behave, its intent, and its identity. This is particularly useful when you are building conversational systems like customer service chatbots.
For instance, let's create a conversational system that's able to generate more technical and scientific responses to questions. Note how we are explicitly telling it how to behave through the instruction. This is sometimes referred to as role prompting.
Prompt:
Output:
Our AI research assistant sounds a bit too technical, right? Okay, let's change this behavior and instruct the system to give more accessible answers.
Prompt:
Output:
I think we made some progress. You can continue improving it. I am sure if you add more examples you might get even better results.
Code Generation
One application where LLMs are quite effective is code generation. Copilot is a great example of this. There are a vast number of code-generation tasks you can perform with clever prompts. Let's look at a few examples below.
First, let's try a simple program that greets the user.
Prompt:
Output:
You can see that we didn't even need to specify the language to use.
Let's switch levels a bit. I want to show you how powerful LLMs can be with a little more effort in designing the prompts.
Prompt:
Output:
This is very impressive. In this case, we provided data about the database schema and asked it to generate a valid MySQL query.
Reasoning
Perhaps one of the most difficult tasks for an LLM today is one that requires some form of reasoning. Reasoning is one of the areas that I am most excited about due to the types of complex applications that can emerge from LLMs.
There have been some improvements in tasks involving mathematical capabilities. That said, it's important to note that current LLMs struggle to perform reasoning tasks so this requires even more advanced prompt engineering techniques. We will cover these advanced techniques in the next guide. For now, we will cover a few basic examples to show arithmetic capabilities.
Prompt:
Output:
Let's try something more difficult.
Prompt:
Output
That's incorrect! Let's try to improve this by improving the prompt.
Prompt:
Output:
Much better, right? By the way, I tried this a couple of times and the system sometimes fails. If you provide better instructions combined with examples, it might help get more accurate results.
We will continue to include more examples of common applications in this section of the guide.
In the upcoming section, we will cover even more advanced prompt engineering concepts and techniques for improving performance on all these and more difficult tasks.
Last updated on April 16, 2023
Last updated