Generative Pre-trained Transformer (GPT)

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What does GPT stand for?

GPT is the acronym for Generative Pre-trained Transformer.

What is GPT?

Generative Pre-trained Transformer (GPT) is a type of transformer, i.e. an advanced deep learning model, designed to comprehend and create human-like text based on the patterns it picks up after learning from vast amounts of text dataset during the pre-training phase.

Why is it called GPT?

The term GPT, i.e. 'Generative Pre-trained Transformer' came about because of the following key characteristics:


  • GPT can develop coherent and contextually relevant text based on the given prompt, ranging from a few words to entire paragraphs. Based on the prompts, GPT can produce responses based on various tones and styles, variations or a continuation of the prompt. Even a slight change in the prompt guarantees a different response generated by GPT.
  • After all, GPT's generational capability stems from the context it detects from the prompt.


  • The pre-training phase is a pivotal point where GPT gains language understanding by predicting the next word in sentences. This step enables it to understand grammar and syntax and gain word knowledge. After this pre-training phase, GPT can be fine-tuned to perform specified tasks.
  • Overall, the objective of pre-training is for GPT to learn the intricacies of language from large datasets.


  • GPT is based on the transformer architecture, a type of neural network architecture that excels at processing sequential data (such as sentences, phrases, paragraphs and so on).
  • Due to this ability, combined with the architecture's efficiency, the transformer architecture is currently the most widely adopted architecture for multiple Natural Language Processing (NLP) tasks.

How does GPT work? Pre-training vs. Fine-tuning

GPT works through a two-step process: pre-training and fine-tuning.

In the pre-training phase, the model learns language by predicting the next word in sentences and gaining an understanding of language structure. No task-specific information is gained here; the learning is limited to understanding the fundamental patterns in a language, such as grammar rules, semantic information and the relationships between words.

During fine-tuning, the model is trained on specific tasks or domains to make it more specialised. Given a prompt, GPT uses its learned patterns to generate coherent and contextually appropriate text.

GPT examples

GPT's capabilities have the world awestruck from the start. Here are just some examples of what it can do

1. Content Generation

The most obvious use of GPT is to generate content- whether it's generating information on topics or writing articles and blog posts from scratch, GPT can do it all within seconds, thereby saving time and effort.

2. Conversational AI

Owing to its human-like conversational style, GPT-powered chatbots and virtual assistants allow for engaging conversations that can assist or simply entertain users.

3. Text completion, expansion and text editing

As it understands the relationships between words after comprehending chunks of data, GPT is capable of completing sentences and elaborating on or summarizing texts. GPT can also suggest, rephrase or directly edit text, increasing readability and aiding users to improve their writing.

4. Language Translation

GPT can break down language barriers thanks to its ability to easily translate text between languages

5. Ideation

Need a brainstorming buddy? Now you've got GPT. Bounce ideas, request feedback or directly ask GPT for ideas and inspiration.

GPT applications across industries

1. Marketing and Advertising

  • GPT assists in creating compelling ad copies, social media posts, and email marketing content
  • Content for social media posts, email marketing, and other ad copies, and even prompts to generate relevant images for the ads, GPT got it.
  • GPT can also generate personalized product recommendations for e-commerce platforms.

2. Healthcare

  • GPT aids in generating medical reports, patient education materials, and summarizing research papers.
  • GPT proves useful to today's overwhelmed healthcare industry by generating and summarizing medical reports, educational materials, and relevant research papers. Chatbots utilising GPT can also assist with preliminary diagnosis, symptom checking, matching patients with relevant doctors, and scheduling.

3. Customer Support

  • Customers have 24/7 support and access to personalized services provided by interactive chatbots using GPT, improving customer experience and satisfaction/

4. Finance and Investment

  • GPT can generate, explain and conclude financial reports and assessments.
  • Moreover, GPT can quickly resolve customer inquiries, saving both the client's and the company's time.

5. Education

  • GPT is a great help to students since it possesses the ability to explain and summarise topics, textbooks and research papers, take quizzes, and provide additional resources in a way that they can easily understand.
  • This, combined with GPT's ability to automate administrative tasks, grade tests and assignments and solve doubts anytime, also makes an educator's job much easier.

6. Travel, Tourism and Hospitality

  • GPT is your tour guide, translator, all-in-one. GPT can help you plan your trip right from the start. Book tickets and hotels and create itineraries customized to your needs, and, to top it off, help with language translation.

7. Gaming and Entertainment

  • The gaming industry is only bound to get more immersive with better storytelling, better characters and better overall experience generated by GPT.
  • GPT can help people power through their writer's block and help with edits and suggestions.

Difference between GPT-3, GPT-4 and everything in between

Pre - GPT 3: GPT 1 and GPT 2


GPT 1, the very first version of GPT, introduced the concept of utilizing transformers for tasks that generate text.

While it demonstrated promising results in generating coherent and contextually relevant responses, it had limitations in terms of fine-tuning and generating highly convincing text across a wide range of topics.


GPT-2 was released in 2019 and made substantial improvements since it was trained over a much larger dataset and overcame GPT -1's obstacle of not being able to produce relevant responses for different topics. OpenAI ,however, made the decision not to release the full version of GPT-2 to the public since they were concerned over potential misuse and spreading fake information.


GPT-3 and GPT-4 are subsequent versions of the GPT model, each more advanced than the last. GPT-3, for example, introduced remarkable capabilities, such as generating human-like text across various prompts and contexts. Each version builds on the architecture and capabilities of its predecessors with increased performance and sophistication.

When the world was introduced to the wonders of AI, GPT 3 was at the forefront of it.

GPT - 3, at its time, was one of the largest language models created and demonstrated a level of human-like response generation that was not seen before, hence leading to its fame in the tech community and, eventually, the public eye.

GPT 3.5

GPT 3.5 served as an intermediary model between GPT 3 and GPT 4. It improved on the previous model while trying to reduce the bias present.

A set of models that improve on GPT-3 and can understand as well as generate natural language or code

GPT 3.5 turbo

The turbo version is a cost-effective model that has been optimized for non-chat applications as well and is available to fine-tune on particular tasks.


In OpenAI's own words, GPT 4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks.

GPT 4 has upgraded on many fronts:

  • Its creative ability,
  • Generating more relevant and coherent responses,
  • Stronger computational skills and programming capabilities
  • Ability to consolidate information from multiple sources for complex queries
  • Increased word limit, and larger memory capacity
  • Conducts more accurate sentient analysis, i.e. detecting potential emotions from the user's input and responding accordingly, leading to a more genuine interaction.
  • Safer and more ethical responses

Meanwhile, new additions to the model have brought about new features:

  1. Previously, GPT 3.5 was only capable of handling text. GPT-4 is a multimodal model that enables it to take both text and image data as inputs.
  2. Gather and consolidate information from multiple sources and even access information from URLs
  3. GPT 4 also possesses the ability to detect dialects and respond to them, too

What's next for GPT? After GPT 4

GPT's capabilities raise ethical concerns regarding misinformation, bias, and misuse. Efforts are being made to address these issues and ensure responsible use. The future of GPT likely involves further enhancements in understanding and generating natural language, as well as improved ethical guidelines to mitigate potential risks and challenges.

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