A Guide to Fine-Tuning GPT Models for Content

Fine-Tuning GPT Models for Content

Content creation via Artificial Intelligence (AI) for marketing isn’t quite as seamless as most businesses hoped it would be. The language is clunky, wordy, and incredibly repetitive. But what if you could train that AI to write exactly like your brand in the blink of an eye? Generative retrained transformer models like GPT-3 or GPT-4 are very close to this. In this blog, we will look at what fine-tuning is, why it’s exciting, and how it could help your business in the future. 

Understanding GPT Models

GPT models can create text, but to do that they have to consume a lot of data to learn how we write things online. Unfortunately, despite all the training these models have been put through, they are still barely coherent and only really make sense on a surface level, as a lot of what they write is repeated and regurgitated and sometimes outright plagiarism. 

The GPT-3 version of the model can write basic fiction and blogs, translate languages, write ads, and more. And the GPT-4 version is said to be more advanced. But those in the know, like writers and marketers can instantly spot a piece written by the models as despite their access to diverse data, they usually write in the same way every time. 

 Fine-Tuning Process

Enter fine-tuning, where you teach an AI model to sound like you. Fine-tuning needs your data in order to learn, and with it can write like you and possess the industry knowledge you have to boot, as long as you give it the tools. This is fantastic if you have a lot of written content in your brand voice already as you could train GPT with pre-existing data. 

To train it well, you’d need many examples; the first step would be to create a spreadsheet of inputs and outputs. To get easy inputs and outputs, you could run your text through Chat-GPT and pretend the original text is the output and the GPT text is the input. This would teach the model to turn its own way of writing into your style. 

You’d also want to feed it industry reports that are up-to-date, customer reviews so it knows what your clients/customers want, and your very best ads. Furthermore, the content you feed the model should also be pretty diverse. For example, different emotional tones, different types of platforms for which you write content, and so on. 

Once you’ve gathered all that content up you have to feed it into a specialized platform using python scripts, or other coding. The model will then scrape through all your data and test itself to make sure it can get as close to your style as possible. In some cases this process is iterative, meaning you can monitor the model’s learning progress and adjust the training data or instructions as needed.

Can I Fine-Tune My Content Yet?

Content creators and marketers alike know how invaluable a tool like this could be, turning generic text into something that is fit for a brand. But unfortunately, this isn’t widely available yet. There isn’t software that lets you directly fine-tune a model and easily spit out what you need. There are tools though that can get you close. 

For example:

  1. Cloud-based Fine-tuning Platforms: Several cloud platforms offer fine-tuning capabilities for GPT models, like OpenAI’s API access and Google’s AI Platform. These platforms require some technical expertise to figure out, like the ability to create JSON files and write python. But they give the most control over the training data and model outputs.
  1. AI Writing Assistants with Pre-Trained Models: AI writing assistants like Jarvis Jasper and ShortlyAI have created their own kind of fine-tuning so that their text sounds more natural. It’s not the brand’s voice specifically, but it’s better than ChatGPT. You can also train assistants on the platform to copy your style by feeding it lots of examples.  

Here’s a breakdown of the pros and cons to help you decide:

  • Cloud-based Fine-tuning Platforms:
    • Pros: You get a lot of control over the type of data and therefore what he model outputs. The model is then more likely to copy your voice. 
    • Cons: You need someone who has the expertise which can cost you, plus it takes some time to achieve it. 
  • AI Writing Assistants:
    • Pros: These are pretty easy to use, and you won’t need to know things like coding. Plus, it’s pretty affordable. 
    • Cons: You won’t get the same accurate results, and have less control over the training as you can’t be sure what the software is telling the model. 

Implementing Fine-Tuned GPT Models in Content and Ads

The possibilities for fine-tuning are endless. Businesses can take advantage of a model trained specifically for their style and knowledge in these areas:

  • Content Creation: better quality blogs that sound like your team are an incredible advantage, as are social media copy, and ads. Content creation would be so much faster and need less overhaul. 
  • Enhancing Advertising Strategies: Fine-tuning allows you to create targeted ad content that speaks directly to different audience segments. The model can analyze past campaign data and competitor strategies to generate ad copy that works. 

FAQs

Q: What are the common challenges in fine-tuning GPT models?

A:  It can take some time for a model to analyze your data, not to mention the hours you’d need to collect all the data to feed it. Then, you’d have to put it together using the correct codes, which only someone versed in coding languages can do well. 

Q: How much data is needed to effectively fine-tune a GPT model?

A: This depends on how complicated the task is that you want your fine-tune models to achieve. And how accurate you want it. But the more data you feed it, the better it will be. For things like writing style, writers are finding that anything over 100 examples is optimal, but preferably 200 or more, of course. 

Q: Can fine-tuned GPT models replace human creativity?

A: You still can’t train a robot to be creative, so you’d still have to babysit what it is creating. Feed the model the ideas, prompt it in the right direction, etc. It can’t make creative and unique content; just sound more like you and has access to better data. 

The Future of AI is Fine-Tuning

Fine-tuning is the direction content creation is likely going when it comes to AI. But it will never replace human creativity and will always need an expert at the wheel. Content creation for marketing is a complex process that requires an abundance of knowledge to do well.