A deep dive into the Generative Pre-Trained Transformer Model and how it can be used in sales.

The use of Artificial Intelligence (AI) has led to extensive changes in information technology. These changes, especially their advantages, also want to be used in other areas. It is, therefore, logical that more and more AI-based solutions are being used and further developed in sales. In this context, many salespeople ask whether ChatGPT for sales could also be a solution.

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The world's most advanced NLP AI model, GPT-3, was developed by OpenAI and is publicly available with the application ChatGPT. So let's take a look at GPT-3, its advantages and disadvantages, and how sales can benefit from it.

The OpenAI team has been developing increasingly advanced models for NLP (neuro-linguistic programming), starting with GPT-1, GPT-2, and culminating in GPT-3. Training on larger and larger amounts of text data has resulted in more human-like speech for these models. As the models grew in complexity, so did the number of parameters they use. By 2020, GPT-3 reached 175 billion parameters, surpassing any other models in the field.

Note: Smart assistants like Siri and Alexa, as well as conversational bots, all use natural language processing similar to GPT-3

Several companies are leveraging GPT-3. On the “gpt3demo” site alone, there are approximately 560 ChatGPT, AI, and GPT-3 Apps and use cases. Harnessing GPT-3 enhances human efficiency. Allowing us to focus on other essential decision-making tasks that machines cannot safely or creatively tackle. To get to know whether ChatGPT for sales might be an option, we first want to have an understanding about the basis of GPT-3.

What is GPT-3?

Rather than explain, I like letting GPT-3 explain itself:

GPT-3 uses a prompt as input, which guides the model's understanding of context and content. Prompts are starting points or topics for the model to generate text about.

In GPT-3, a prompt is a piece of text provided as input to the model. The input then generates a response based on that input. It guides the model's understanding of the context and content of the text it generates. That is, starting point or a topic for the model to generate text about.

For example, in our case, the prompt is “ What is GPT-3?”. In GPT-3, the terms "prompt" and "input" are interchangeable.

Better, I also asked the model to explain itself to non-tech audiences, referring to the previous prompt:

GPT-3 has the ability to generate text connected to previous prompts. This is due to its ability to understand and generate text based on the context and input provided. This means that GPT-3 can generate text consistent with the topic or theme of previous prompts.

The advantages and disadvantages of ChatGPT (for sales)

This already leads us to the advantages and disadvantages of ChatGPT, or more precisely GPT-3, as well as the possibility of using ChatGPT for sales. So let's first take a closer look at one of GPT-3's strengths:

Generating human-like text

Generating human-like texts is one of the key strengths of GPT-3. The model understands and generates text with high accuracy and fluency. The text is indistinguishable from text written by humans. Therefore useful for tasks such as writing, content creation, and chatbots.

After fine-tuning, GPT-3 uses go beyond specific types of writing, e.g., news articles or messaging and can even generate text in different styles and tones, such as formal and casual. Content Creator verwenden ChatGPT bereits um Bildunterschriften, Schlagzeilen und Zusammenfassungen zu erstellen und so Zeit und Mühe zu sparen. GPT-3 kann aber auch Texte für Chatbots generieren und auf eine Vielzahl von Fragen antworten.

At first glance, this may sound beneficial when it comes to the use of ChatGPT for sales. But as the proverb says: there are always two sides of the coin and we do not want to ignore the second side. That's why we'll continue here with one disadvantage of GPT-3.

Lack of data basis in the generative model

GPT-3 doesn't use a retrieval-based model; instead, it is a generative model. A retrieval-based model retrieves a response from a pre-defined set of responses based on the input, while a generative model generates a response based on the patterns it has learned from the training data. GPT-3, as a generative model, can generate a wide range of responses to a given input, but it may not be able to provide an exact response to a specific question.

An important keyword here is the training data. The extent and quality of this depend on whether the model can answer the question correctly. For a large number of questions and scenarios, ChatGPT can benefit from the training data used in GPT-3. However, there are also some specific topics for which the model is not trained properly. The model lacks, for instance, specific, practical databases for sales.

What is the solution for ChatGPT for sales?

ChatGPT's human-like texts can basically be used by anyone free of charge so far. However, various field tests have already shown that ChatGPT is not designed for sales. Why not? As explained at the beginning, ChatGPT is based on the GPT-3 model, which is trained on a large amount of data and data points. Although this data also includes a component of knowledge from sales, this is more rudimentary data and not field-tested sales knowledge. Reliable sales data is simply missing for this application.

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