In recent months, ChatGPT has taken the world by storm with its unparalleled language creation capabilities. And now, with the imminent release of GPT-4, the potential applications of this technology are greater than ever.

In this post, we will look at the ChatGPT and GPT-4 models for business.

GPT (Generative Primed Transformer) is a family of language models that uses deep learning techniques to generate human-like language. Models are pre-trained on large amounts of textual data such as web pages, books, and articles. This pre-training allows the models learn patterns and language relationships, making them better at creating human-like text.

The original idea of ​​GPT models was to configure them specifically for translation from one language to another. Open AI took this idea much further.

The result: GPT generates unique and very specific pre-query text results. And – as we’ve all experienced over the past few months – ChatGPT has generated incredible buzz because it’s… amazingly human.

Year after year, the GPT model was refined and constantly improved to achieve linguistic excellence. On the latest model, the OpenAI team spent 6 months making GPT-4 more secure and compliant than the previous version. ChatGPT, which was open to the public, played an important role here as the team worked extensively on user feedback.

The latest GPT-4 model is capable of handling dozens of language tasks, creating unique content of any form and context, as well as handling complex non-linguistic tasks, such as working with SQL and internal databases. Since it also works with images and has been fine-tuned since the hype of ChatGPT, it is more flexible and has many more possible use cases. We will look at some of them below.

However, even after great progress, GPT models still sometimes appear to provide false or non-aligned (morally stitched) information to users.

Now that the GPT-4 model is available, anyone can check what it’s good at and what it’s not. To access the latest GPT-4, people must subscribe to ChatGPT Plus for $20 per month. With this subscription, users can choose between two chatbots, one that uses GPT-3.5 and one that uses GPT-4.

As we mentioned before, GPT was trained on a sinking amount of data. But that doesn’t mean it’s limited to what he already knows. GPT models can trained on additional data (in other words, adapted) and learning patterns and structures in the input data. This makes GPT highly context aware and works wonders at recognizing patterns and relationships between words and phrases.

Otherwise.

These two factors enable GPT in many ways.

Being a large language model, GPT is designed to be used to:

Q&A (AI Chatbot)

One of the most obvious use cases is answering questions.

ChatGPT is specifically designed to interact with users in chat. People can ask questions and get answers right away. How is that? GPT is not only able to understand what the user means, but also generates an appropriate response thanks to its vast database.

So, a business that might want to leverage this GPT power and create a GPT-based chatbot, it is possible to connect your own trusted sourcewhich is your website, knowledge base, internal documentation, etc. This way, the chatbot will look to it as a source and use its language generation capabilities to provide answers.

So, a business that might want to leverage this GPT power and create a GPT-based chatbot, it is possible to connect your own trusted sourcewhich is your website, knowledge base, internal documentation, etc. This way, the chatbot will look to it as a source and use its language generation capabilities to provide answers.

Automate routine work

GPT already has billions of data it has been trained on. Not only does it use this amount of data as a source, but it can also categorize it.

Why not use this superpower to work on your own data?

Let’s imagine that we have connected our own database, for example Knowledgebase, to GPT. First, it can read stored information and then analyze it to sort or group similar information. Second, GPT can run through chat to quickly find this information and generate a text response in seconds. Just like Google’s Bard.

Instead of the user searching for the information they need, reading it and taking the next steps, GPT does all the work in a convenient way.

Another example of using GPT for automation would be: analysis of product feedback provision, support calls and transcriptions. Likewise, GPT may process the information on behalf of support agents.

Writing help on specific topics

This is where the use cases for marketing are truly endless.

With GPT, users can create new content or rewrite user-submitted content as a writing aid for business content or predefined topics. Users may only rewrite or create content for specific business purposes or predefined topics and may not use the application as a general content creation tool for all topics. Examples of business content include proposals and reports.

And in fact, the use cases here are not limited. Brand new business examples show how versatile GPT applications can be.

When ChatGPT was released, businesses actually sat back and watched the storm rise.

3 months later, with the GPT-4 release, the situation was completely different. a number of companies partnered with Open AI to build their solution before the GPT-4 or ChatGPT API was released.

Some early adopters include:

• Microsoft confirmed that versions of Bing using GPT were actually using GPT-4 before the official release. On March 17, 2023, Microsoft announced further integration of GPT-4 into its products, revealing Microsoft 365 Copilot, which is “embedded in the applications that millions of people use: Word, Excel, PowerPoint, Outlook, Teams etc.” Basically, GPT is now built into commonly used tools in the workplace.

• Snap Inc. has introduced My AI for Snapchat+. My AI offers Snapchatters a friendly, customizable chatbot that offers recommendations and can even write haiku for friends in seconds. Snapchat, where communication and messaging are everyday behaviors, has 750 million monthly Snapchatters.

• Stripe, which uses GPT-4 to scan business websites and provide a summary to customer service staff.

• Duolingo built GPT-4 into a new language learning subscription tier. The team is creating a GPT-4-powered system that will extract information from company documents and serve financial analysts.

• Khan Academy uses GPT-4 to build a teaching chatbot that the organization calls “Khanmigo”. Access to the “research phase” chatbot is provided free of charge to students and teachers in the 500 school districts who have “partnered” with Khan Academy. Public access is offered only to a limited number of users selected from a waiting list; After adoption, a $20 monthly fee is required to use the technology.

• Instacart is expanding the Instacart app to allow customers to ask about food and receive inspiring, shoppable answers. This uses Instacart’s proprietary AI alongside ChatGPT and product data from their 75,000+ retail partner store locations to help customers find ideas for open-ended shopping goals like “How can I make great fish tacos?” or “What is a healthy lunch for my kids?” Instacart plans to launch Ask Instacart later this year.

One of the most important options, giant companies, famous start-ups and even the biggest payment system have trusted GPT.

Each of these companies invested and joined the game in order not to miss the competition. They chose their use case and put it live collect user feedback.

Perhaps this approach is the most logical in the context of GPT. How else do you test technology at work?

The time to act is now. As more businesses adopt GPT models, those that fail to keep up risk being left behind. By leveraging this technology now, you can gain a competitive edge and position yourself as a leader in your industry.

As a chatbot development company, we have experience with many chatbot projects as well as automation solutions over time. Here are some of our best practices that we recommend following when building a GPT solution:

Limit GPT to avoid errors and chatbot failure.

The more specific it is, the more predicted results you will get.

Select a specific use case.

The more specific it is, the more predicted results you will get.