Hey folks, another day, another article about prompt engineering. Today, we’re going to be going over some of the methods that are used to test prompts for a large language model. In particular, we’re looking at ChatGPT prompts, with our focus being on things that you can do before needing to involve other people to test. So, how can we do that?
Prompt engineers can use various tests to evaluate ChatGPT prompts, including coherence, relevance, diversity, and language model scoring. These tests can help ensure the quality of prompts before deploying them to the public and can be performed by the prompt engineer before seeking outside assistance.
Table of Contents
- Why Testing ChatGPT Prompts is Important
- Types of Tests Used To Evaluate ChatGPT Prompts
- Designing and Conducting Experiments For ChatGPT Prompts
- Best Practices for Improving the Quality and Effectiveness of ChatGPT Prompts
Of course, we’ll cover some of the other ways to do testing, but it’s best that a poor product never goes out the door.
By the time you get to the end of this article, you should have a good understanding of how to test ChatGPT prompts, the benefits of different tests that you can use, how to design and conduct experiments, and finally, the best practices for improving the quality and effectiveness of ChatGPT prompts.
Whether you’re a ChatGPT prompt engineer, a developer, or someone just interested in AI and natural language processing, this article will help you evaluate better what you’re doing. So let’s get going.
Why Testing ChatGPT Prompts is Important
I’ve already visited a number of online libraries for prompts; unfortunately, the quality of the prompts there leaves much to be desired. Often, people are sharing ideas rather than functional prompts. It’s essential to create high-quality prompts that will actually help people if you want to be successful in prompt engineering.
The purpose of testing is to ensure that once a prompt is released into the wild, it will actually work for the intended users. For example, I released a library of prompts for bloggers to help them better create and promote their blogs. The time required to write the article was short compared to the amount of time needed to test the prompts properly. In addition to testing the prompts, it was necessary to understand the needs of the bloggers using them.
In addition to testing prompts, there is another step that we will not be discussing at the moment. That step is ensuring that what you’re creating is something that’s actually useful. This requires understanding the problems of the people that you’re trying to meet better. Fortunately, ChatGTP is often a very good tool for that.
When doing testing, we are looking for:
- Diverse and relevant output generated text, programs, and images that are related to the problem.
- Simple prompts.
- Prompts that can be used on GPT 3.5 and GPT 4.0 and any other model that’s currently in use.
- Prompts that are correctly being evaluated by ChatGTP every time that they’re executed. The primary difference between an AI like ChatGTP and programming is that ChatGTP does not give exactly the same results every time.
- An increase in user’s Trust and Satisfaction by delivering high-quality and accurate texts. It’s this increase in user experience that will bring people back to using the prompts that you create.
- Prompts that cater to specific needs of consumers. We need to make sure that those needs are actually being met by the prompts that we create. For instance, we might ask ChatGTP to write articles, create outlines, or act like a textbook. We need to understand the limits of the outputs of ChatGTP to know what’s possible.
- Some prompts rely on feedback loops to improve texts or programs that are given to ChatGTP to process. We need to make sure that those feedback loops always increase the value of the text on each iteration. This will require running the prompts for multiple iterations for testing.
What happens if you don’t test? You produce junk. Sometimes it’s usable junk, but generally, it’s just junk. As a prompt engineer, you can also damage your reputation by producing poor-quality output. So testing can mitigate the risks not only to your consumers but also to your own reputation.
In the next section, we will go over some of the tests that can be used to evaluate ChatGPT prompts and their limits and strengths.
Types of Tests Used To Evaluate ChatGPT Prompts
As we’ve already established, testing ChatGPT prompts is crucial for ensuring that we’re putting out a product that our consumers are able to use. So let’s go over some of the tests that can be used to evaluate a prompt
Statistical Testing Through Repeated Evaluation
It is an unfortunate truth that ChatGPT prompts do not always produce the same output every time. Because of that, you will need to test individual prompts multiple times to make sure that they are producing the intended output.
An essential step to this is to use different chats to test the same prompt multiple times. The reason for using different chats to test the same prompt is that the chat history itself acts as a crosstalk mechanism. What I mean by crosstalk, in this case, is that the chat history that has come before affects the output of later prompts. So it’s necessary to eliminate this variable to ensure reliable testing.
It is worth noting that there may be an issue with consumers if their chat history interacts with our prompt. Unfortunately, this is not something that we can easily control or test. However, requesting at the beginning of your prompts that ChatGPT ignores all previous instructions can help mitigate possible crosstalk problems.
Hopefully, over time we will develop additional methods to test for crosstalk or have definitive ways to mitigate it.
For prompts that use a feedback loop like the prompts Creator prompt, it’s necessary to use the feedback mechanism to test the prompt multiple times to make sure that the output continues to get better through each iteration.
User Evaluation Testing
Another type of testing, of course, is to actually give the prompt to your consumers and ask for feedback. This is very important in terms of making sure that the prompt is producing what the end consumer wants. By the time you hand the prompt to them, it should be working properly as far as you understand its purpose.
The main purpose of user testing is to evaluate the user experience and to identify areas for improvement.
Users can often be recruited through forums such as Reddit subreddits dedicated to artificial intelligence or ChatGPT. It is important to note that users on those forums must be treated with politeness and care and that, unfortunately, there are a number of bad apples there.
If possible, you may want to try finding someone closer to you to see if they can test your prompts as well. But it is important that the person doing the testing for you actually uses the prompts for the purpose they have been created for. For instance, someone who uses Twitter regularly should be the person who evaluates a prompt to help them make tweets.
Forms are often a good place to receive feedback, although the feedback is not qualitatively as good as a questionnaire or interview. If you are able to find users who are willing to fill out a questionnaire or speak with you about the prompt, that is the best way to get user feedback.
Treat any feedback you receive from users as an opportunity to identify areas for improvement.
Takeaways
- Evaluate each prompt multiple times using different chats to prevent crosstalk from chat histories.
- Be aware of possible issues of users’ chat history creating cross-talk with your prompts. Requesting at the beginning of a prompt that it ignore all previous instructions can mitigate some of the possible cross-talk interference.
- Feedback loop prompts need to be tested through multiple iterations to make sure that the text they are producing improves.
- The main purpose of user testing is to gather information about the user’s experience.
- Although this can be used to find problems, its primary purpose is to ensure that you are meeting the needs of the user with the prompt you have written.
- Users can be recruited on forums such as Reddit. Interviews or questionnaires can be used to understand the user’s experience.
- Using consumers closer to home to evaluate your prompts is also invaluable.
- Treat all feedback that you get from users as an opportunity to identify areas for improvement.
Designing and Conducting Experiments For ChatGPT Prompts
Designing experiments to test ChatGPT prompts can be challenging, depending on the user base available for evaluation. For instance, if your user base is a group from Reddit, your ability to collect sufficient data for statistical analysis may be limited. However, if you are working for a larger company, you may have the means to pay for surveys to be conducted.
The most important thing to keep in mind is that gathering any feedback regarding your prompts is valuable. Whether that feedback comes from testing the prompt on ChatGPT or from users, it’s all valuable.
Therefore, we need to cover the key considerations when designing experiments, selecting the metrics that we will measure, designing test scenarios, and collecting data.
Considerations When Designing Experiments
So what factors do you need to consider when designing experiments:
- Objectives:
- Define the objectives of testing prompts, for example, to evaluate output quality or measure efficiency in terms of tokens.
- Sample size:
- Consider statistical significance and aim for at least 20 repeated experiments to determine the percentage of failures.
- This is especially important when initially testing prompts.
- Controlling variables:
- Consider significant variables, such as the version of ChatGPT being tested (e.g. GPT 3.5 vs GPT 4.0) to ensure prompt functionality across different versions.
- Evaluate prompts in new chats to prevent crosstalk from chat history.
- Randomization:
- Avoid having evaluators receive prompts in the same order. If the users are always tired when they receive the prompt it may lead to bias due to fatigue or boredom.
- Use randomization to reduce this bias.
Picking the Metrics That You Will Use For Evaluation
What is a good metric? Metrics should be relevant to the objectives of the experiment and they should be measurable. So what are some examples?
- Coherence: How well does the output of the prompt match with what we are trying to achieve? Is it logically connected together does it contain all the information that we requested?
- Relevancy: How well does the output match what was requested?
- Diversity: How much variety is there in the outputs that the prompt produces?
Let’s look at examples of each of these.
Coherence
I wrote a prompt to generate Twitter tweets. In general, the prompt works well. It’s supposed to always include a branding hashtag and a URL. However, I have found that about one time out of 10, it fails to include the URL. As of today, I have not figured out how to fix that problem, but it’s important that I am aware that it exists.
Relevancy
Oftentimes, when requesting an outline for a potential article, I find that my prompt is not generating an article outline that matches my vision. In part, that occurs because I’m usually writing about ChatGPT, and its knowledge base is limited to 2021. Knowing that this is an issue, I have to spend extra time going over article outlines.
Creating a better prompt to produce article outlines that match what the author is trying to convey is a long-term goal for me. I need more input however from other authors to create a better prompt to generate the outline.
Diversity
Generally speaking, we want a prompt to produce basically the same output with a given input every single time. I call that the McDonald’s effect. Challenges to that occur between different GPT models, for instance. Currently, I have an issue that I’m aware of with my article title writer. Its behavior with GPT 4 is not as robust as with GPT 3.5. I expect that a rewrite of this prompt will hopefully take care of the issue.
Defining Test Scenarios and Collecting Data
Defining your test scenario is the next step in designing experiments to test Chat GPT prompts. Your test scenarios should be designed to simulate real-world use cases while keeping in mind the objectives that you have defined earlier. These test scenarios should be relevant to the objectives to ensure effective testing. It is important to record specific information during the testing process. Below is a partial list of what to record.
- Input: The input that was given to the prompt.
- Output: The output that was produced by the prompt.
- Metrics: The metrics that were used to evaluate the output.
- Test conditions: The hardware and software setup used for the test. ChatGPT 3.5 GPT4,etc.
- Results: The results of the experiment.
Recording the data obtained from testing provides valuable insights into the performance of the prompts. It requires taking the time to analyze the data and understand its implications. While user feedback is also essential and should be recorded, keeping track of the testing data is just as important.
Tips To Avoid Common Problems and Challenges When Conducting Experiments
So let’s lay out the most common problems that occur when doing experiments and how to avoid them.
- Create a standard testing procedure that includes your objectives and metrics for each testing procedure.
- Use a statistically significant sample size, with a minimum of 20.
- Control your variables and test prompts for both GPT 3.5 and GPT 4.0.
- If using users to evaluate prompts, ensure they receive the prompts in a random order.
- Whenever possible, use real-world inputs that are relevant to your objectives.
- Collect and record all relevant data to ensure reproducibility and transparency.
- Analyze your data to determine your next step
Designing and conducting experiments to test chat GPT prompts helps to ensure the prompts are effective and meet the needs of their intended users.
Best Practices for Improving the Quality and Effectiveness of ChatGPT Prompts
So in this article we’ve discussed testing and evaluating chat GPT prompts. Our purpose was to ensure that they are both effective and engaging at meeting the needs of their intended users.
To make sure that we create these types of prompts here are some best practices and tips to keep in mind.
- Be sure to clearly define the purpose and objectives of the prompt. What are you trying to achieve with this prompt, and what outcome do you expect to see? Having a clear goal will make it easier to reach it.
- Use simple and concise language that is easy for ChatGPT to understand and process. Long prompts tend to produce inconsistent and diverse outputs. Avoid technical jargon and complex sentence structures. Be careful with your modifiers to ensure they do not have double meanings.
- Provide clear and specific instructions with context. Specify the format you want the output in and give examples of the desired output, especially if you are having problems with diverse outputs. More detailed and specific instructions are more likely to give you the intended output.
- Test the prompt multiple times under different scenarios to ensure its consistency and accuracy. It is crucial to evaluate prompts on fresh chats each time to avoid cross-talk from chat histories.
- Use user feedback and insights to refine and improve your prompt. By listening to your users and understanding their needs and preferences, you can make better decisions about how to improve the quality and effectiveness of your prompt.
Here are a few real-world scenarios for you to consider.
- If you are creating a prompt for writing articles, consider the following:
- Specify the topic, audience, and tone of the article.
- Keep in mind that chat GPT can only output 400 to 700 words per prompt, so use outlines and request small portions of the outline to be written at a time.
- For creating a prompt for generating outlines, make sure to:
- Specify the length and words of the article that the outline is for, as well as its structure and purpose.
- When creating a prompt for chat GPT to act like a textbook, keep in mind:
- Specify the topic, level, and style of the textbook.
- Provide specific examples of other textbooks to give chat GPT guidelines on the structure and content of the text.
That’s all for today I hope that you enjoyed this article and learn something from it. I also sincerely hope that you’re able to better create prompts for yourself and for others. So get out there and test test test.