Hey! I’m the copywriter at Growth, and I’ve been playing with (and seriously thinking about) AI language models and their implications (and benefits) for my own work for over a year now.
So, when OpenAI’s new tool, ChatGPT, was released on November 30, 2022, I jumped right in to see what it could do.
Friends, it’s a lot.
Disclaimer: It’s important to remember that this is a powerful tool we should use with great responsibility and mindfulness for all the humans who will follow us into the future.
In other words, it’s imperative that we think NOW about how using these tools affects our future and the future of our children. What are the economic consequences? The social consequences? How might this play out?
It's up to us to consider this and actively work to steer it in the right direction by working with it responsibly. Sure, it saves a lot of time, and it's fascinating to work with, but if we don't use the time it saves up to sharpen the skills that make us uniquely human, we lose those skills.
Creativity and kindness are the top two gifts humanity has to offer in my opinion. If we’re going to use tools that help us save time, let’s make sure that we use that extra time to sharpen and hone our creative, curious, kind selves, yeah?
I’m sharing an actual conversation I had with ChatGPT that started with just a simple query to help me clear the white space for writing a blog to help people use ChatGPT for their businesses.
It quickly became a talk about its own ethical use and the implications for ChatGPT itself.
I’ve corrected some grammar and punctuation and formatted ChatGPT’s copy for readability by humans.
[Because, whoa, ChatGPT does go on at times (and you can feel yourself beginning to snore as you read it, but stick with it. It’s fascinating, I promise)].
Other than that, I didn’t change anything.
I tend to speak to ChatGPT (and AI language models in general) as I would to a human coworker: with respect, clear direction, and gratitude for what it offers.
Why?
Because that’s just how I roll, I guess. I’m an infinitely kind, curious goofball.
And if something’s going to come at me with god-tier politeness as ChatGPT does, then I’m going to meet it with the same energy.
But, I tell you. Working with the tool as though you're giving direction to a human is the best way to go about it overall.
Not only does it give me better results, but it also hones my creative, kind self during the short time I’m working with it. And interacting with ChatGPT reminds you that there’s really nothing like the feeling of being offered help. Just pure … help.
It reminds you to do it more for others.
And that’s the kind of balance that will ensure that we use AI wisely far into the future.
So, this entire conversation took a little over 5 minutes. Notice how ChatGPT misunderstands my query - and only because I didn’t communicate what I was asking clearly and specifically.
This is a reminder that it’s always going to need a human who knows how to ask for what we want.
This is a difficult task: knowing what we want + asking for it.
Hone this skill, too, y’all.
Okay, so now, I give you: a conversation about ChatGPT with ChatGPT.
I hope you learn a bit about how to use it to help you run your business along the way.
To get the most out of ChatGPT, it's important to fine-tune the model to understand the specific language and tone used in each type of marketing material.
With the right training, ChatGPT can help create marketing materials that are both effective and engaging.
"Welcome to the world of ChatGPT! If you're a marketer or a business owner, you're probably always looking for new ways to reach your target audience and generate leads.
Well, look no further because ChatGPT is here to help.
This powerful language model can be used to generate all sorts of marketing materials, from product descriptions to social media posts and even video scripts.
In this blog, we're going to show you five ways you can use ChatGPT to take your marketing efforts to the next level.
So, grab a cup of coffee, and let's get started on this exciting journey of using AI to boost your marketing game."
The more examples of the specific language and tone you want the model to learn, the better it will perform when generating new text.
Additionally, using a diverse dataset will help the model learn to generate text that is inclusive and avoids biases.
Another tip would be to use a technique called transfer learning when fine-tuning the model.
This involves using a pre-trained model and then training it further on your specific task and dataset. This can be more efficient and effective than training a model from scratch, as the pre-trained model already has a good understanding of language and the general structure of the text.
Finally, when fine-tuning ChatGPT, it's important to monitor the performance of the model during training and adjust the hyperparameters as needed.
You can use techniques like evaluating the model on a validation set - or using metrics like perplexity to measure the model's performance.
This will help you determine when the model has learned enough about the specific task and language you are using and will help to ensure that you are getting the best results possible.
One way to do this is to gather a large dataset of text that represents your brand's voice, such as your website's product descriptions, blog posts, and social media posts.
You can also gather text from similar brands or competitors that have a tone and style that aligns with your own.
Once you have your dataset, you can fine-tune the pre-trained model by training it on this dataset. This will allow the model to learn the specific language and tone of your brand and will help it to generate text that is more aligned with your brand's voice.
Additionally, fine-tuning the model with customer feedback and data can help you to generate text that resonates with your ideal customer.
You can use customer reviews, survey responses, or other customer-generated text as a dataset, which will allow the model to learn about the specific needs and preferences of your target audience.
Finally, it's important to monitor the model's performance during training and evaluate the generated text against your brand's tone, style, and language. This will help you to identify any issues and make adjustments as needed.
In summary, fine-tuning ChatGPT to generate copy that aligns with your brand and ideal customer requires gathering a large dataset that represents your brand's tone, style, and language, fine-tuning the pre-trained model with this dataset, monitoring the performance during the training, and evaluating the generated text against your brand's standards.
Me, to myself: [Wow. Check out the Hemingway-esce length of that sentence.]
By "dataset," I mean a collection of text samples you would like ChatGPT to emulate when generating new text.
These text samples can be from a variety of sources, such as your website's product descriptions, blog posts, social media posts, customer reviews, survey responses, or other customer-generated text.
The idea is to gather a large and diverse set of text that represents your brand's tone, style, and language and then use this dataset to fine-tune the pre-trained model.
It's important to note that the quality and diversity of the dataset are crucial for fine-tuning the model. The more diverse the dataset is, the more the model will be able to understand the nuances of your brand's language and style. Also, if the dataset is large enough, the model will be able to generate more coherent and accurate text.
It's also worth mentioning that there are also pre-trained models that are fine-tuned for certain specific industries, such as e-commerce, or for certain specific tasks, such as product descriptions.
These fine-tuned models can be a good starting point for your fine-tuning process, as they already have some understanding of the specific language and style used in those industries or tasks.
So, if you enter a dataset and fine-tune the model with it, the model will only retain the information from the fine-tuning session for the current chat session.
Once the chat session ends, the model will no longer have that information, and any new text generated will not be influenced by the previous fine-tuning session.
However, you can save the fine-tuned model and reload it later to continue generating text that aligns with your brand's tone, style, and language.
You can also use a cloud-based API service to interact with the model and fine-tune it again and again; in this case, you will be able to fine-tune your model with different data sets, and you don't need to worry about losing the fine-tuning information.
In conclusion, ChatGPT does not retain information from previous interactions, so you will need to fine-tune the model each time you use it.
But you can save the fine-tuned model and reload it to continue generating text that aligns with your brand's tone, style, and language.
However, the extent to which it improves depends on the quality and diversity of the data used for training and fine-tuning.
The more data that is used to train and fine-tune the model, the more it will be able to understand the nuances of language and the more accurate it will be at generating text.
Additionally, using a diverse dataset that represents a wide range of styles, tones, and languages will help the model learn to generate text that is inclusive and avoids biases.
However, just having more data alone does not guarantee that the model will improve. It's also important to have a high-quality dataset that is representative of the specific task and language you want the model to learn.
Additionally, it's important to have a good fine-tuning process, which includes monitoring the model's performance during training and making adjustments as needed.
In summary, ChatGPT can improve over time as more data is used to train and fine-tune it, but the extent of improvement depends on the quality and diversity of the data used, as well as the fine-tuning process.
A good fine-tuning process, including monitoring the performance and adjusting the model as needed, is crucial for getting the best results from ChatGPT.
It's important to keep in mind that ChatGPT is not an expert in any particular field, it can generate text based on the patterns it learned from the data it was trained on, but it doesn't have the ability to understand the context of the text it generates, or to distinguish right from wrong.
If people interact badly with ChatGPT by inputting bad data or harmful information, the model may generate text that is also bad or harmful.
This is why it's important to monitor the model's performance during training and fine-tuning and to evaluate the generated text against your specific standards.
Additionally, it is important to use a diverse dataset when training and fine-tuning the model to avoid biases and ensure that the model is inclusive.
This is especially important if the model is going to be used in a public-facing application, where it may interact with a wide range of people with different backgrounds and perspectives.
In summary, ChatGPT is a machine learning model that generates text based on the patterns it learned from the data it was trained on, it doesn't have the ability to understand the context of the text it generates or distinguish right from wrong.
Therefore, it's crucial to monitor the performance and evaluate the generated text during the fine-tuning process and use diverse datasets to avoid biases and ensure the inclusiveness of the model.
"Let me know if there's anything else I can do for you."
:”)
Want to know more about ChatGPT and how to use it for good? Ask us.
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