A Comprehensive Guide to GPT-3: Understanding the Technology, Capabilities, Limitations, & Real-world Applications of ChatGPT

17 min readJan 14, 2023

ChatGPT does not need any new Introduction. It certainly created a storm during the end of 2022 and showed the potential of Generative AI.

Earlier DALL-E 2 created graphical sensation to an extent but chatGPT unlocked the GOD Mode. Well, now we know and experienced the power of chatGPT and other similiar tools, it's important to know how it is built, what it is and everything you need to know about this technology.

What is GPT-3?

GPT-3 is a new and very advanced computer program that can understand and generate human language. It can be used to write articles, answer questions, and even write computer code. It has been trained on a large amount of text data so it can understand the context and meaning of the words. It is considered to be one of the most powerful language models currently available.

GPT stands for “Generative Pre-trained Transformer” A transformer is a type of neural network architecture that is particularly good at processing sequential data, such as natural language. The “generative” part of the name refers to the model’s ability to generate new text that is similar to the text it was trained on. The “3” in GPT-3 refers to the fact that it is the third generation of this type of model developed by OpenAI. It is an upgraded version of its predecessors GPT-1 and GPT-2, with more advanced capabilities and larger dataset used for training.

GPT-1 and GPT-2 are the previous versions of GPT-3.

GPT-3 (Generative Pre-trained Transformer 3) is the latest and most advanced version of the GPT model, with a much larger dataset used for training and even more advanced capabilities than its predecessors. It is considered to be one of the most powerful language models currently available.

In summary, each generation of GPT model has a larger dataset and more advanced capabilities than the previous one, GPT-3 is the latest and most advanced version, with a larger dataset used for training and more advanced capabilities than its predecessors.

What does train by a dataset means?

Training a machine learning model using a dataset means using a set of data to teach the model how to perform a specific task. The dataset is made up of examples of input and the corresponding correct output for the task at hand. In the case of GPT-3, the task is natural language processing, and the input is text data, such as sentences and paragraphs, and the output is a predicted next word or a response to a question.

During the training process, the model is presented with the input data and the corresponding correct output, and its internal parameters are adjusted to minimize the difference between its output and the correct output. This process is repeated multiple times with different examples of input and output, allowing the model to learn the patterns and relationships in the data. Once the training is completed, the model has “learned” how to perform the task and can be used to make predictions on new, unseen input data.

The no of times the training process is repeated for each generation of GPT model can vary depending on various factors such as the size of the dataset, the complexity of the task, and the specific training algorithm used. However, in general, each generation of GPT models was trained for a large number of iterations, in some cases, it could be in hundreds of thousands or even millions of iterations.

How much data is Large Dataset?

The dataset used to train GPT-1 was approximately 40GB of text data. GPT-2 was trained on a much larger dataset, approximately 570GB of text data. GPT-3 was trained on an even larger dataset, approximately 570GB of text data and 175 billion parameters. It’s important to note that these numbers are approximate, and the exact size of the dataset used to train each version of GPT may vary depending on the source.

Basics of Natural Language Processing:

Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that deals with the interaction between computers and human languages. The goal of NLP is to develop algorithms and models that can understand, interpret, and generate human language.

NLP can be divided into two main areas: Understanding and Generation. Understanding natural language includes tasks such as language translation, text summarization, and sentiment analysis. Generation of natural language includes tasks such as text summarization, text completion, and machine translation.

GPT-3 is a model that was trained for natural language processing, it can understand and generate human-like text, answering questions, and even coding. It can be fine-tuned for a wide range of natural language processing tasks such as language translation, summarization, and question answering, among others.

So, can we call chatGPT as Generative AI?

Yes, GPT (Generative Pre-trained Transformer) is a generative AI model, as the name suggests. Generative models are a type of machine learning models that can generate new data that is similar to the data it was trained on. In the case of GPT, it is trained on a large dataset of text data and can generate new text that is similar to the text it was trained on.

Generative models can be used in a wide range of applications such as natural language processing, computer vision, and speech recognition, among others. GPT-3 is particularly useful for natural language processing, it can generate human-like text, answer questions, and even write computer code.

It’s important to note that GPT-3 is also a pre-trained model, which means that it has already been trained on a large dataset and can be fine-tuned for specific tasks without the need for additional training, this makes it more efficient and cost-effective to use.

Here are some examples of different types of natural language processing (NLP) tasks:

Language Translation: The task of converting text from one language to another. For example, translating a sentence from English to Spanish or vice versa.
Text Summarization: The task of creating a condensed version of a text while preserving its main ideas. For example, summarizing a news article or a book chapter.
Sentiment Analysis: The task of determining the emotional tone of a text, such as whether it is positive, negative, or neutral. For example, analyzing customer reviews to determine the overall sentiment towards a product.
Text completion: The task of generating a text by completing a given text or a sentence. For example, completing a sentence with the next word or a phrase.
Named Entity Recognition: The task of identifying and classifying named entities in text, such as people, organizations, and locations. For example, identifying the names of people, organizations, and locations mentioned in a news article.
Question Answering: The task of answering questions based on a given text or a document. For example, answering questions like “who is the president of the United States?” or “when was the Eiffel tower built?”

These are just a few examples of the many different types of NLP tasks that exist. GPT-3 is able to perform a wide range of NLP tasks and it can be fine-tuned for specific tasks, which makes it a versatile tool for various NLP applications.

Workflow of GPT:

The workflow of GPT can vary depending on the specific application, but generally, it follows these steps:

Data collection: The first step is to collect a large dataset of text data that will be used to train the model. This dataset should be representative of the type of text that the model will be working with in the future.
Model training: The next step is to train the model using the collected dataset. The training process involves presenting the model with input text data and corresponding correct output and adjusting its internal parameters to minimize the difference between its output and the correct output. This process is repeated multiple times with different examples of input and output.
Fine-tuning: Once the model is trained, it can be fine-tuned for specific tasks such as language translation, text summarization, and question answering. Fine-tuning involves using a smaller dataset that is specific to the task and adjusting the model’s parameters to optimize its performance for that task.
Inference: After the model is fine-tuned, it can be used to make predictions on new, unseen text data. This process is called inference. For example, the model can generate text, answer questions, or translate text from one language to another.
Evaluation: The last step is to evaluate the model’s performance. This can be done by comparing the model’s output to the correct output on a test dataset. This allows to measure the model’s performance and identify areas where it can be improved.

Type of data used to train Model

The type of data that is collected to train GPT models is typically text data, such as articles, books, and websites. This data can be collected from various sources, such as the internet, libraries, or publicly available datasets. The data is usually in the form of plain text files, but it could also be in other formats such as PDF or HTML.

How to collect data

To collect the data, various web scraping tools can be used to automatically download text data from websites. For example, Python libraries such as `BeautifulSoup` and `Scrapy` can be used to extract text data from HTML pages. Other tools such as web crawlers and APIs can also be used to collect data.

Once the data is collected, it is typically pre-processed to clean and format it properly. This can include removing special characters, lowercasing all text, and removing any irrelevant information. This pre-processing step can be done using scripting languages such as Python or using specialized pre-processing tools.

After pre-processing, the data is then ingested into the model training process. This can be done using various machine learning libraries such as TensorFlow or PyTorch, which provide APIs for loading and processing the data. The data is usually loaded into memory in the form of tensors, which are multi-dimensional arrays that can be processed by the model.

The process of collecting, pre-processing and loading the data into the model can be quite complex and time-consuming, but it’s a crucial step in the training process as the quality of the data will affect the performance of the model.

Architecture of GPT

The GPT-3 model consists of a transformer-based neural network architecture, which consists of an encoder and a decoder. The encoder takes in the input text and converts it into a set of hidden representations, which are then passed to the decoder. The decoder generates the output text based on these hidden representations.

The transformer architecture is based on self-attention mechanisms, which allow the model to weigh the importance of different parts of the input when generating the output. It also includes multi-layer perceptrons, which are used to make predictions based on the input and hidden representations.

The model is trained using a large dataset of text data and fine-tuned for specific tasks. Once trained, the model can be used for a wide range of natural language processing tasks such as language translation, summarization, and question answering.

Where does NLP fits in AI?

Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) that deals with the interaction between computers and human languages. It involves developing algorithms and models that can understand, interpret, and generate human language.

NLP is closely related to other subfields of AI such as Computer Vision (CV) and Speech Recognition (SR). These fields all deal with processing and understanding different types of data that are generated by humans. CV deals with image and video data, SR deals with audio data, and NLP deals with text data.

NLP is also closely related to the field of machine learning (ML). Many NLP tasks, such as text classification, sentiment analysis, and language translation, involve training models on large datasets of text data and then using these models to make predictions on new, unseen text data.

In summary, NLP is a subfield of AI that deals with the understanding and generation of human language, it’s closely related to other subfields of AI such as Computer Vision and Speech Recognition and it’s also closely related to the field of machine learning. NLP models are trained using machine learning techniques on large datasets of text data, and then used to make predictions on new, unseen text data.

Tools like GPT-3 and other GPT-based text-generating tools have several things in common:

They are all based on the transformer architecture, which is particularly well-suited for processing sequential data like text.
They are all pre-trained on large datasets of text data, which allows them to generate human-like text.
They can all be fine-tuned for specific tasks, such as language translation, text summarization, and question answering.
They are all capable of generating text, answering questions, and performing other natural language processing tasks.

What makes some tools special is their training dataset, fine-tuning, and the number of parameters which leads to the model’s capabilities and performance. For example, GPT-3 is considered one of the most advanced text-generating tools currently available because it was trained on a massive dataset and has 175 billion parameters, which allows it to generate highly human-like text and perform a wide range of NLP tasks.

Another example is, fine-tuning a pre-trained GPT model for a specific task can also make it special, for instance, fine-tuning a GPT model for a specific domain such as legal or medical can make it more proficient in that specific domain.

It’s important to note that there are other text-generating tools available, some of which are based on different architectures, and have their own unique capabilities and trade-offs.

Model Server: This component is responsible for serving the pre-trained GPT model to clients. It receives input text and generates output text by making predictions using the model. It also handles fine-tuning the model for specific tasks.
Data Store: This component is responsible for storing the large dataset of text data that is used to train the GPT model. It can be implemented using various types of data storage technologies such as SQL or NoSQL databases, depending on the specific requirements of the application.
Data Processing: This component is responsible for pre-processing the data before it is used to train the model. This can include cleaning and formatting the text data and converting it into the appropriate format for the model.
Training: This component is responsible for training the GPT model. It uses the pre-processed data and the appropriate machine learning libraries and algorithms to train the model.
Management and Monitoring: This component provides the management and monitoring of the system, including the logging, metrics and monitoring of the system’s performance.
Frontend Application: This component is responsible for providing an interface for users to interact with the GPT-based tool. It can be a web application, a mobile application, or a command-line interface (CLI), depending on the specific requirements of the application.

Building GPT model application on AWS

Building a GPT-based tool on AWS would typically involve the following steps:

Collect and pre-process the data: Collect a large dataset of text data and pre-process it to clean and format it properly. This can be done using scripting languages such as Python or using specialized pre-processing tools.
Training: Train the GPT model using the pre-processed data. This can be done using various machine learning libraries such as TensorFlow or PyTorch and using AWS services like Amazon SageMaker or EC2 instances with GPU support.
Model hosting: Host the trained model on a model server, such as Amazon SageMaker, to serve it to clients.
Data storage: Store the pre-processed data and the trained model in a data store, such as Amazon S3 or Amazon DynamoDB, for future use.
Create a web or mobile interface: Create a web or mobile application that allows users to interact with the GPT-based tool. This can be done using AWS services like Amazon S3, Amazon API Gateway, and AWS Lambda.
Management and Monitoring: To monitor the system and its performance, use AWS services such as Amazon CloudWatch or AWS CloudTrail.

Here’s a sample code in Python that uses the BeautifulSoup library to scrape a list of basic AWS services from the AWS documentation website:

import requests
from bs4 import BeautifulSoup

url = "https://aws.amazon.com/products/"

response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")

services = []

for li in soup.find_all("li", class_="aws-text-box"):
    services.append(li.find("h2").text)

print(services)

This code makes a GET request to the AWS documentation website, using the requests library, and then uses BeautifulSoup to parse the HTML response.

It then searches for all li tags with the class "aws-text-box", which corresponds to the list of services, and extracts the text of the h2 tag inside each li tag. This text corresponds to the name of the service.

The names of the services are then appended to the services list and printed to the console.

It’s important to note that scraping data from websites is against some website’s terms of service, so please make sure to check the website’s terms of service and use the data appropriately.

Difference between Adaptive AI and Generative AI

Adaptive AI is a type of Artificial Intelligence (AI) that is able to adapt its behavior based on new data and feedback. The goal of adaptive AI is to create systems that can learn and improve over time, without the need for explicit programming.

An example of adaptive AI is a self-driving car, which uses sensor data and machine learning algorithms to adapt its behavior based on the environment and the actions of other vehicles. As the car encounters new situations, it can adjust its behavior to ensure safety and efficiency.

Generative AI, on the other hand, is a type of AI that is able to generate new data that is similar to the data it was trained on. Generative models are used in a variety of applications such as natural language processing, computer vision, and speech recognition.

GPT-3 is a good example of Generative AI, it was trained on a large dataset of text data and can generate new text that is similar to the text it was trained on. The main difference is that generative AI focuses on creating new data based on the data it was trained on, while adaptive AI focuses on adapting its behavior based on new data and feedback.

In summary, Adaptive AI is focused on adapting its behavior based on new data and feedback, while Generative AI is focused on generating new data based on the data it was trained on.

Other NLP Models

There are many other natural language processing (NLP) models that are used for various tasks such as language translation, text summarization, and sentiment analysis. Some examples of other NLP models include:

BERT: A transformer-based model that is trained on a massive dataset of unannotated text data, BERT is particularly well-suited for tasks such as question answering and natural language inference.
RoBERTa: A variant of BERT that is trained on even more data and fine-tuned using a different pre-training technique. It’s considered as one of the state-of-the-art models for NLP.
ULMFiT: A model that is trained on a large dataset of text data, ULMFiT is particularly well-suited for tasks such as text classification and language translation.
Transformer-XL: A model that is designed to handle long-term dependencies in text data, Transformer-XL is particularly well-suited for tasks such as language translation and text summarization.

Advantages of GPT over other Models

The advantages of using GPT-3 over other NLP models include:

Scale: GPT-3 was trained on a massive dataset of text data, which allows it to generate highly human-like text and perform a wide range of NLP tasks.
Fine-tuning: GPT-3 can be fine-tuned for specific tasks using relatively small amounts of task-specific data, which makes it more efficient than other models that require large amounts of task-specific data to perform well.
Flexibility: GPT-3 can be used for a wide range of NLP tasks, including language translation, text summarization, and question answering.
Human-like text generation: GPT-3 generates text that is very similar to human-generated text, which makes it particularly well-suited for tasks such as chatbots and language generation.

GPT-3 is considered one of the state-of-the-art language models currently available, it has several advantages over other models:

Scale: GPT-3 was trained on a massive dataset of text data, which allows it to generate highly human-like text and perform a wide range of natural language processing (NLP) tasks.
Fine-tuning: GPT-3 can be fine-tuned for specific tasks using relatively small amounts of task-specific data, which makes it more efficient than other models that require large amounts of task-specific data to perform well.
Flexibility: GPT-3 can be used for a wide range of NLP tasks, including language translation, text summarization, and question answering.
Human-like text generation: GPT-3 generates text that is very similar to human-generated text, which makes it particularly well-suited for tasks such as chatbots and language generation.

When comparing to other state-of-the-art models, GPT-3 is considered to be one of the most advanced models in terms of its capabilities and performance. However, it’s important to note that other models such as BERT, RoBERTa, and T5 may perform better on specific tasks, such as question answering. Additionally, GPT-3 has a large number of parameters, which makes it less efficient and more computationally expensive to run than other models.

In summary, GPT-3 is considered to be one of the most advanced and capable language models currently available, but other models such as BERT, RoBERTa, and T5 may perform better on specific tasks and have more efficiency.

Limitations of GPT

GPT-3 is a highly advanced language model, but it does have some limitations. Some of the limitations of GPT-3 include:

Lack of commonsense knowledge: GPT-3 lacks commonsense knowledge, it is not able to infer context or understand idiomatic expressions, which can lead to errors or inaccuracies in its output.
Bias: GPT-3 was trained on a massive dataset of text data, which includes biases that can be reflected in its output. This can be mitigated by fine-tuning the model with a diverse dataset.
Computational cost: GPT-3 has a large number of parameters, which makes it computationally expensive to run. This can make it difficult to use in real-time applications or on low-power devices.
Lack of interpretability: GPT-3’s decision-making process is not transparent; it is difficult to understand how the model arrived at a particular output.
Data privacy and security: GPT-3 requires access to large amounts of data to work, which can raise concerns about data privacy and security.
It’s not the best model for every task: Although GPT-3 is a highly advanced model, it’s not the best model for every task, other models such as BERT, RoBERTa, and T5 may perform better on specific tasks.

What about GPT-4?

GPT-4 is not a released model, it has not been officially announced by OpenAI. Therefore, it’s difficult to say what new capabilities or improvements it would have over GPT-3. However, it’s likely that GPT-4 would have even more data and parameter capacity than GPT-3.

OpenAI has stated that the GPT series will continue to improve with each new generation, so it’s reasonable to expect that GPT-4 would be even more powerful than GPT-3. It could potentially have more advanced capabilities such as even better understanding of idiomatic expressions, improved commonsense knowledge, and less bias. However, it’s important to note that the GPT-3 already has a large number of parameters, so further increasing that number would have a significant impact on computational cost, which is one of the main limitations of GPT-3.