GPT Chatbots: Digital Brand Reputation

The use of AI models is a sea change in architecture and nothing like traditional programming. In the past, humans were required to input data into a computer system or network designed to transform and output it according to specific use cases. In other words, the system was designed for a specific purpose or set of objectives. When the purpose shifted, the humans operating it programmed additional features and functionality into the system.

An AI model, on the other hand, is designed to provide output for specific use cases while learning on its own. Whereas a programmer may have previously been required, GPTs can now interface with humans who “train” the model using natural language. The more training data fed into it, the more accurate the AI model. In other words, the tech is becoming “smarter” and reducing the need for human intervention and programming. For example, I used ChatGPT itself to generate much of the code for this chat application, saving me time I could then use to write this article.

In 2018, GPT-1 fed more than 100 million example datasets. A year later, GPT-2 was trained on more than a billion, and GPT-3 had 150 times that amount. In 2022, GPT-3.5 was used to create the controversial ChatGPT. See ChatGPT, neural network, and DALL-E.

GPTs change the future in two primary ways:

In today’s web search model, the end-user acts as the point of processing agency. A traditional query process looks like the following:

The AI interpretation model alters the process, causing the AI engine to act as an agent on behalf of the end-user. The AI query process looks like this:

In a scenario where customer service reps are handling queries on behalf of a company, the GPTs are able to perform much of the work previously managed by human efforts:

Customer service reps now move into the role of quality control auditors and managers. Individuals who previously conversed with customers now become the “watchers” of the conversational AI. They review conversational threads, managing and correcting response quality and accuracy. The company workers shift into a role of continuous quality improvement.

Your business can build a GPT chatbot to integrate into its operations. In the following example, a chatbot is designed as my virtual company brand rep.

Design an interface.

Visit OpenAI to register an account, sign up, and subscribe for an API key.

Products and services used in this example:

• OpenAI Subscription
• Web Server running HTML/CSS/PHP

The GPT acts in accordance with the prompt instruction. For my example GPT rep, the key line of code telling it how to act is found here in the code:

In addition, GPTs can utilize uploaded documents (Word documents, PDF files, JSON files, etc.) containing text for specific answers you want it to generate. For example:

The GPT can also be programmed to generate responses from website URLs, making it possible answer questions in a given manner and from live data sources. In the coming months and years, software companies will release APIs to connect GPTs to virtually every data source imaginable, including large internal data repositories like SharePoint.

My brand rep is connected to the following data sources:

Over time, you can connect additional data sources to your GPT through APIs, making it possible for the AI dataset to improve its body of knowledge. As the system responds, the responses can be tuned (trained) for higher accuracy and quality.