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What is a GPT?

Learn how AI models built on the generative pre-trained transformer (GPT) interpret and create human-like content.
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The role of GPT in AI

GPT stands for generative pre-trained transformer and is a family of neural network models that analyze data and interpret and produce human-like text, images, and sounds. People and organizations use GPT to summarize long text and meetings, translate languages, create written communication, write code, generate images, and answer questions in a conversational tone.

Key takeaways

  • GPT is a deep learning neural network that analyzes prompts made up of natural language, images, or sounds to predict the best possible response.
  • By repeating the prediction process multiple times, GPT is able to create human-like content and engage in long conversations.
  • GPT is based on the transformer architecture which interprets the meaning of content by turning words, images, and sounds into mathematics.

  • GPT is effective because it’s trained on massive datasets, including large text corpora.

  • GPT is transforming how people get things done by simplifying research, reducing busywork, accelerating the process of writing words and computer code, and boosting creativity.

  • A few GPT use cases are chatbots, content creation, sentiment analysis, computer code creation, data analysis, and meeting summaries.

  • OpenAI continues to invest in GPT, and in the future, organizations can expect better output, more transparency, less bias, and greater accuracy.

What GPT is and how it works

GPT is a deep learning neural network that analyzes prompts made up of natural language, images, or sounds to predict the best possible response based on its interpretation of the input. To do this, it’s trained with massive datasets using hundreds of billions of parameters. GPT references that learning to weight the importance of different components in a sequence, such as words in a sentence or parts of images or sounds. The weighting allows it to infer relevance and context so that it can generate content that makes sense with the prompt.

History of GPT

In 2018, OpenAI released the first generation of GPT, which was built on that architecture GPT-1 was trained on over 1.5 billion parameters and can generate text, answer questions, translate languages, and summarize text, but it has a hard time understanding context and struggles with long passages of text. 

Every couple of years since then, OpenAI has released a new version of GPT each trained on successively larger datasets. With each release, the technology improves its ability to understand context and write fluently and coherently. It continues to add new skills, such as creating computer code, performing tasks with little or no examples, and analyze vast amounts of data. 

Training overview

To be effective, GPT must be able to parse and interpret a myriad of prompts and requests. It prepares for this by training on massive datasets, including large text corpora, using unsupervised deep learning, a subset of machine learning. In unsupervised learning the model teaches itself to find patterns in unlabeled data without guidance from humans. GPT uses computer vision to identify and understand objects and people in images.

GPT can also be trained for very specific scenarios, such as for an industry, like banking or law. In these instances, supervised learning is used, which means that training data is labeled by humans.

Basic GPT architecture

GPT is built on the transformer architecture, which uses the self-attention mechanism to analyze different components of a prompt and their relationship to each other to interpret context and meaning. For example, the word “cloud” can refer to condensed vapor in the sky or, as in cloud computing, a technology platform. People and GPT determine which version of the word is appropriate by evaluating the meaning of the other words surrounding it in a sentence or paragraph.

The transformer architecture is able to do this by turning words and their meaning into mathematics. It breaks up text, images, and sounds into smaller pieces called tokens. The tokens are assigned a vector, which encodes meaning. The encoded vectors, called embeddings, are then sent through an attention block where they exchange information and make updates to the vectors as appropriate. Once GPT has determined the meaning of the prompt, it produces a prediction in the form of a probability distribution and suggests the next word, image, or sound in the sequence. By repeating this process over and over, it can write long passages or carry on a conversation.

Key components

The architecture is made up of two parts:

  • Encoder. The encoder is the part of the system that breaks down text, images, and sounds into mathematical embeddings. Each embedding is assigned a weight, which tells it how relevant it is to the context and meaning. The embeddings are then compared to each other using the self-attention mechanism to further refine their meaning.

  • Decoder. The decoder uses the vectors and weights to determine possible outputs and predict the best one. Because the most current versions of GPT have been trained on so much data, they’ve gotten quite good at using this process to write fluent and coherent text. 

The benefits and challenges of GPT

GPT has the potential to transform how you and your organization work, helping you save time and money. But there are also risks with using this technology without careful guardrails. It’s critical to always carefully vet the information you get from GPT or any other AI system to confirm it’s accurate and ethical.

Benefits

 
  • Simplify research. GPT can scour the internet and/or other data sources and provide a summary of what it found and sources if requested.

  • Enhance computer code. Developers use GPT to help them write new code or simplify what they’ve already written.

  • Write faster. One of the most popular ways to use GPT is as a writing tool. It can quickly synthesize a lot of information and develop reports, blog posts, emails, and other written materials.

  • Reduce busywork. GPT can do things like summarize meetings, translate languages, and answer questions, empowering you to spend more time on more impactful tasks.

  • Boost creativity. In addition to writing poetry, GPT can quickly generate lots of different ideas, making it a great tool for brainstorming. 

  • Customize to your business. GPT can be trained to meet the unique needs of different organizations and industries.

Challenges

 
  • Bias. Like all AI models that rely on human-created data, the biases inherent in that data may make it into the GPT output. For example, AI models may assume that certain roles in society, like scientist, are only performed by men because most of the historical data is about male scientists. 

  • Inaccuracies. Because GPT generates output based on a prediction, it isn’t always correct. Asking it to reference known materials or training it on your organization’s knowledge base can help, but a human should always review the work for accuracy.

  • Cybersecurity. Bad actors are using GPT and other AI models to create convincing phishing emails, develop malware, and analyze organizations for vulnerabilities. Training employees to recognize phishing emails can help lower your organization’s risk. It’s also important to implement cybersecurity solutions that can detect anomalies and block malware.

  • Intellectual property violations. The output from GPT may include images or copy created by another person or organization. Before publishing anything created by AI, confirm your organization has rights to the content and use citations appropriately.

  • Ineffective prompts. Getting a good output from GPT requires a well-structured prompt. It may take training and trial and error to develop a prompt that gets you the results you’re hoping for.

  • Impenetrability. Because GPT is built using a deep learning model, it’s difficult to know how it comes up with its responses, which is another reason to review its output carefully before using it.

Common GPT use cases

GPT models can perform a broad range of tasks, and organizations continue to find new ways to use them in their organizations. Here are a few things to try:

Content creation. Use GPT to help you write copy, generate memes, and produce images.

Chatbots and conversational agents. Because GPT can understand and respond in natural language, it’s a great tool for chatbots. 

Language translation. GPT does a good job translating languages, although it’s always best to confirm accuracy with a native speaker before posting it on your website or other public space.

Sentiment analysis. GPT can help you analyze customer reviews, social media posts, or other text to understand how people feel about your brand, products, and services.

Recommendations. Before a big trip, consider asking GPT to recommend restaurants, hotels, and attractions to visit. With the right parameters it can help you develop a list of good options.

Research. Because GPT is good at summarizing information, it’s also a great research tool. It can help reduce the number of websites, reports, and other documents that you need to review to find what you’re looking for. Just be sure to ask for sources so you can validate the information you get.

Meeting and document summarization. GPT can save lots of time by providing summaries of meetings or long documents.

Code creation. GPT knows many computer languages and can generate relevant snippets of code or explain, in conversational language, what the code is doing.

Data analysis. Uncover trends and key insights in large datasets with the help of GPT.

The future of GPT

OpenAI continues to make big investments in GPT. GPT-4o was released in 2024. The “o” in the name stands for omni because the model can process and generate audio, text, and visual. GPT-4o mini is a smaller model that supports text and audio. It performs better than previous GPT models, such as GPT-3.5, but is more cost-effective.

And you can continue to expect improvements in model efficiency and capabilities, such as:
 
  • Larger models with better performance. Future iterations of GPT are likely to be even larger and trained on more parameters, allowing them to understand and generate context with greater nuance and complexity.

  • Greater fine-tuning and customization. There will be more advanced techniques for fine-tuning models to specific domains or industries, improving their ability to generate relevant and accurate content tailored to particular fields. Individuals will also be able to customize the model to their needs.

  • Better contextual understanding. Advances in understanding and managing long-range dependencies will help models provide more precise and contextually appropriate responses.

  • More advanced multimodal capabilities. Models will get better at understanding and generating content based on diverse inputs, such as text, images, and audio.

  • Enhanced explainability and interpretability. Efforts will be made to make the decision-making processes of GPT models more transparent, providing insights into how they generate responses and the rationale behind their outputs.

  • Ethical and responsible AI development. Ongoing research and development will focus on reducing biases in GPT models to ensure more equitable and fair outputs. Enhanced methods for detecting and mitigating harmful content, misinformation, and inappropriate outputs will be a priority to ensure responsible use of the technology.
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Frequently asked questions

  • GPT is a generative AI model that uses deep learning to interpret and produce human-like text, images, and sounds.
  • The transformer architecture is a deep learning neural network that allows AI models like GPT to interpret natural language and generate original text, images, and sounds. It does this by analyzing different components of an input and their relationship to each other to encode context and meaning. This allows it to predict what comes next in a block of text, an image, or a sound.
  • GPT is an AI model that uses deep learning to interpret human-like text, images, and sounds in order to generate new content, provide data analysis, or summarize information. It does these and other tasks effectively because it was trained with massive datasets using hundreds of billions of parameters. Pre-trained means it was trained on this data before it was released to the public.