It's obvious that GPT-3 is reinventing the way we think about customer service. With its ability to generate highly human-like output, it provides a game-changing tool for automating customer support and services, offering unprecedented levels of convenience and cost savings.
But as with any new technology, the real question remains: can GPT-3 be trusted to deliver reliable results? In this blog post, we'll explore what exactly makes the Artificial Intelligence behind GPT-3 so powerful (hint: AI + Natural Language Processing), explore how it can be deployed in customer service scenarios, discuss potential challenges that may arise with using such advanced technology, and finally introduce EmailTree.ai as an innovative solution for overcoming these challenges.
By the end of this post, you should have a much better understanding of whether or not adopting GPT-3 into your Customer Service strategy could work for you.
What is GPT-3?
GPT-3 is an artificial intelligence (AI) system developed by OpenAI that uses natural language processing (NLP) to generate human-like text. It stands for Generative Pre-trained Transformer 3, and it is the third version of a transformer model created in 2017. GPT-3 has garnered significant attention as one of the most powerful AI systems ever built, capable of producing high-quality written content without much guidance or input from humans.
How has GPT-3 been built?
GPT-3 is a deep learning system, meaning it uses several layers of neural networks to process data and generate output. It is powered by over 175 billion parameters (pieces of information), which are stored in a large database and used to generate natural language outputs.
GPT-3 also uses transfer learning, which means that the model can take advantage of prior knowledge from other tasks to more quickly learn new concepts. This combination of deep learning and transfer learning makes GPT-3 different from other AI systems, as it can better understand context and produce more human-like results with fewer resources than traditional machine learning models require.
Different use cases of GPT-3
GPT-3 has the potential to transform almost every area of business and industry, from real estate and finance to marketing and customer service.
1. Marketing: GPT-3 can be used to write engaging copy for websites, emails, brochures and more. It can also generate ad creative ideas, such as headlines and visuals, to be used in paid campaigns.
2. Customer service: GPT-3 can answer customer inquiries with natural language processing, reducing the need for human agents. It can also help companies better understand customer sentiment by analysing their comments and feedback.
3. Real estate: GPT-3 can be used to generate descriptions for real estate listings. It can also aggregate data from multiple sources and create detailed reports on market trends and pricing information.
These are just a few of the many possibilities that GPT-3 has to offer in terms of customer service, marketing, and beyond.
How Can GPT-3 Impact Customer Service?
In terms of customer service, GPT-3 has the potential to radically change the way we interact with customers. With its ability to produce generic replies quickly, GPT-3 can respond to customers in a more efficient manner than ever before.
However, while efficiency and automation are important when it comes to customer service, it is still essential that customer satisfaction is kept as the primary aim.
With automation not always able to provide personalized answers beyond predefined responses, GPT-3 can never completely supplant human interaction when it comes to customer service.
While it may be able to handle certain types of interactions, there will always be a need for human empathy and understanding in customer service. It is likely that GPT-3 will be most effective when used as a supplement to, rather than a replacement for, human customer service representatives.
Understanding the Role of Context in AI Performance
Context is important in AI performance because it helps the AI model understand the meaning and relevance of the information it is processing. In the case of customer service, context is particularly important because it helps the AI model understand the specific needs and concerns of the customer.
For example, consider a GPT-3 chatbot that is designed to answer questions about a product. If the chatbot is not able to take into account the context of the conversation, it may provide generic responses that are not relevant to the customer's specific question.
On the other hand, if the chatbot is able to consider the context of the conversation, it can provide more targeted and relevant responses to the customer's specific questions.
One way to improve the ability of an AI model to understand and use context is to train it on a large and diverse dataset that includes a wide range of customer interactions and questions. This can help the model learn to recognize and understand the different ways in which context can affect the meaning and relevance of a given piece of information.
The Importance of Representativeness in Training Data
Training data plays a crucial role in the development of any artificial intelligence (AI) model. It is the input used to "teach" the model how to perform a particular task, and the training data quality can significantly impact the model's performance.
One aspect of training data that is particularly important is representativeness, which refers to the degree to which the data accurately reflects the real-world conditions under which the model will be used.
The importance of representativeness in training data becomes particularly apparent when we consider the potential consequences of using biased or unbalanced data.
For example, if an AI model is trained on a dataset that is heavily biased towards one particular group or demographic, it is likely to perform poorly when applied to a more diverse population. This can result in unfair or even harmful outcomes, such as inaccurate decision-making or inequitable treatment.
On the other hand, training data that is representative of the real-world conditions under which the model will be used can help to ensure that the model is effective and accurate in its performance.
For example, when developing an AI model to predict which questions will most likely be asked by customers, it is important to include a diverse range of customer inquiries in the training data. This will help the model learn to recognize the various factors that can influence what customers ask, such as the complexity of the topic, potential solutions offered, and customer needs.
In summary, representativeness is a key factor to consider when developing AI models. Ensuring that the training data is representative of the real-world conditions under which the model will be used can help to improve the model's performance and reduce the risk of misleading or unfair outcomes.
A Real Case Study on GPT-3's Understanding of Context
One of the key features of GPT-3 is its ability to understand and use general context in generating text.
To illustrate this capability, we will present two actual cases from one of our customers: BWM Mediasoft, as well as an imaginary case.
Let's start with the imaginary case
In this example, GPT-3 was able to understand the general context of the conversation (the user's request for a refund) and provide relevant information and steps based on that context. This demonstrates GPT-3's ability to understand and use general context in generating text.
However, it is important to note that GPT-3's understanding of context is not limitless. While it may be able to understand and use general context to generate text, it may not have specific knowledge about the details of a company's refund policy or procedure. In this case, the AI must be trained to follow the company's refund procedures.
Please note: It's clear that this reply to the customer is inadequate from a customer satisfaction perspective. The customer is looking for a refund and instead of offering meaningful assistance, the GPT3 reply only instructs them to send an email and check the refund policy.
A customer service agent should be there to help their customers, not just send a generic email asking them to check a website.
Real Use case #1 from BWM Mediasoft
Input: "I need to update my credit card information so I can renew my antivirus. Can you help me with this"
EmailTree's AI response ( AI custom response ) :"Good evening Mr. XXX,
We thank you for your message.
The ESET renewal order has been sent to your attention.
If you are going to pay from this email using your new credit card, this card will be registered in our system for your next licence renewal.
We remain at your disposal to answer your questions and to help you with your choice.
Again the GPT-3 AI was able to generate a generic response based on the context of the question ("I need to update my credit card information so I can renew my antivirus") but did not take into account any specific information about BMW Mediasoft's processes or procedures.
On the other hand, EmailTree's AI was able to generate a response tailored to BMW Mediasoft's process and procedure by using a custom knowledge base.
This reply was specifically designed to help customers renew their subscriptions and update their credit card information quickly and easily. The purpose of this reply is to drive revenue and improve overall customer satisfaction.
Real Use case #2 from BWM Mediasoft
Input: "Hello, Following my license renewal, would it be possible to send me the corresponding invoice? Thank you in advance"
EmailTree's AI response ( AI custom response ) : "Good evening Mr. XXX,
We thank you for your message.
Please find below a link that will allow you to access your invoice in PDF format from your customer account.
Click here to access the link.
We are at your disposal!
In this case, GPT-3 was able to generate a generic response that provided basic steps for obtaining the invoice, but was not able to provide the customer with a direct link to access their invoice.
On the other hand, EmailTree's AI was able to offer an enhanced response by providing a direct link to the customer's invoice in the response. This direct link was generated using EmailTree's AI technology and its customized knowledge base, so the customer could quickly access their invoice without having to search for it manually.
By providing this enhanced response, customers are able to easily find the information they need, resulting in better customer satisfaction.
The potential for GPT-3 in customer service scenarios is immense, as it can quickly generate natural-sounding replies to customer inquiries. It can even be used to launch automated customer support chatbots, eliminating the need for manual responses.
However, there are potential challenges that may arise with using such advanced technology. For example, GPT-3 is only capable of generating generic answers that can't always capture the nuances and complexities of customer service scenarios.
EmailTree.ai provides a unique solution for overcoming these challenges, allowing businesses to reap the benefits of GPT-3 without compromising on quality.
By leveraging a custom knowledge base, EmailTree's AI is able to reply to customers with accurate and up-to-date information about specific business’s processes and procedures. This can result in better customer satisfaction since customers are more likely to receive responses that meet their needs.
Businesses can maximize customer satisfaction by taking a mindful approach to using GPT3, combining it EmailTree.ai and customizing their knowledge base.
This combination of automation and personalization helps ensure customers feel heard and respected, leading to improved satisfaction levels.