GPT-3 : Use Cases, Advantages, and Limitations

Dec 13, 2022

The Generative Pre-trained Transformer 3 (GPT-3) is a deep learning-based language model that generates human-like text.

GPT-3 requires a small amount of input text to generate large volumes of relevant and sophisticated machine-generated text such as code, stories, poems, and so on.

That is why it has become such a trendy topic in natural language processing.

In this article, I will define GPT-3, as well as discuss its applications and significance.

Continue reading to learn about its applications and how it works.

GPT-3 : Use Cases, Advantages, and Limitations

GPT-3 is a machine learning model that describes itself as a friendly, self-taught, thinking and writing robot that can learn and improve on tasks without being explicitly programmed to do so.

Open AI released GPT-3 in 2020 as a better and larger successor to their previous language model (LM) GPT-2.

In terms of producing text that appears to be written by a human, GPT-3 outperforms all previous models.

With over 175 billion machine learning parameters, it is the largest neural network ever produced, while the largest trained language model prior to GPT-3 was Microsoft’s Turing NLG model, which had 10 billion parameters.

GPT-3 : Use Cases, Advantages, and Limitations

One of the major components of NLP is natural language generation, which focuses on generating human language natural text. But creating human-readable content is difficult for machines that are unfamiliar with the complexities and nuances of language.



1- Coding and Summarization

GPT-3 can generate any text structure, not just human language text. It can also generate text summaries and even programming code automatically.

Copilot, which is powered by GPT-3, is used by developers to write code (it generates 40% of newly written code)



2- Writing

GPT-3 has been trained to generate realistic human text from internet text in order to create articles, stories, news reports, and dialogue.



3- Automated Conversations

GPT-3 is also used for automated conversational tasks, such as responding to any text that a person types into the computer with a new piece of text that is contextually appropriate.



4- Risk Management

GPT-3 is used to generate risk ratings automatically based on risk title, causes, preventing controls, consequences and impacts, recovery and mitigating controls, and other factors.



5- Transcriptions

GPT-3 can be used to automatically summarize meeting transcripts.



6- Game Design

GPT-3 can be used in game design, where developers use voice commands sent to GPT-3 to help them create and design augmented reality objects.

Few-shot learning is the practice of feeding a machine learning model a small amount of training data in order to make predictions.

It is well known that standard fine-tuning techniques necessitate a large amount of training data for the pre-trained model to accurately adapt to tasks.

On the other hand, few-shot learning can be used in NLP with language models that have been pre-trained on large datasets.

And after being trained on some examples, these models can then comprehend related and previously unknown tasks.

Zero-shot learning (ZSL) is the process of training a model to do something it was not explicitly trained to do.

It is necessary to provide some kind of descriptor for an unknown class in order for a model to predict that class without training data.

However, different zero-shot methods may have different rules for what types of class descriptors are allowed, which is why it is critical to provide relevant context to obtain accurate results.

GPT-3 : Use Cases, Advantages, and Limitations

ChatGPT is an AI-powered chatbot that uses conversation context to teach NLP how to converse with humans.

It is intended to understand natural language and respond appropriately, making it easier for users to communicate with computers.

Importance of ChatGPT:

  • Building complex chatbots that can provide detailed information or carry out tasks.
  • Providing personalized recommendations based on users needs.
  • Saving time and effort by automating simple tasks.
  • Answering questions quickly and accurately.


GPT-3 : Use Cases, Advantages, and Limitations

The most obvious advantage of GPT-3 is that it can generate large amounts of text, making the creation of text-based content easier and more efficient.

It is usually beneficial in situations where having a human on hand to generate text output is impractical or inefficient, or where automatic text generation that appears human is required.

It can be used to translate languages, write essays, summarize text, and answer questions, among other things.

GPT-3 is not the first model to focus on natural language generation and transforms data into human-like language, but it is currently the most effective.

It is incredible not only for its numerous applications, but also for demonstrating the power of artificial intelligence and offering an early look at future possibilities.

GPT-3 is not free of flaws and limitations, but it is a step forward in the field of NLP, which is concerned with machines’ ability to understand, respond to, or produce human-like language.

GPT-3 : Use Cases, Advantages, and Limitations

The main problem with GPT-3 is that it cannot constantly learn. Because it has been pre-trained, it does not have an ongoing long-term memory that learns from each interaction.

Furthermore, GPT-3 is incapable of explaining and interpreting why specific inputs result in specific outputs, which is a limitation shared by all neural networks.


GPT-3 can comprehend text and write like a human, which opens up almost limitless possibilities for its application.

However, it is far from perfect, which is why OpenAI plans to build larger, less limited, and more domain-specific versions of its models on a wider range of texts, as well as with more use cases and applications.