ChatGPT on ChatGPT

There is a lot of media time (both commercial and social) currently being given to AI systems and to ChatGPT in particular, so we decided to take a look at ChatGPT from the point of view of a content producer for example for a blog column.

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ChatGPT on ChatGPT

There is a lot of media time (both commercial and social) currently being given to AI systems and to ChatGPT in particular. We decided to take a look at ChatGPT from the point of view of a content producer for example for a blog column. To this end we asked ChatGPT to generate a 900 word blog for a technical Audience on what ChatGPT is. This is what happened.

Accessing ChatGPT

To get started with ChatGPT you need to have an account with OpenAI using your email address. This is very easily done from the OpenAI home page. Once you have an account you can log into OpenAI’s website and scroll for the ChatGPT link down the page and click on start chatting.

Is ChatGPT free?

ChatGPT is free (for the time being) and allows you to ask questions of the research version of ChatGPT. As such the answers you get back may or may not be correct or what you expect. This is because ChatGPT is still learning and several warnings are given as you ue ChatGPT about the material generated. For example:

ChatGPT on ChatGPT

ChatGPT also makes it clear how it collects data to improve the quality of the system using human intervention.

ChatGPT on ChatGPT

There is a paid for version of ChatGPT – it offers faster performance, priority access to new features and access to the service even when demand is high (which can be limited for those using the free plan). The paid for version also uses the latest version of the GPT algorithm (GPT-4) (on which ChatGPT is based) which is more advanced than that used in the free version (which is GPT 3.5).

Is ChatGPT any good?

To try ChatGPT out we asked it to write a blog on ChatGPT for a technical audience. The following is what was produced:

User: Explain ChatGPT for a Technical Audience in 900 word blog


Title: Unleashing the Power of ChatGPT: A Deep Dive into the GPT-3.5 Language Model


In recent years, natural language processing (NLP) and artificial intelligence (AI) have made significant strides, transforming the way we interact with technology. One remarkable breakthrough in this field is ChatGPT, an advanced language model developed by OpenAI. In this blog post, we will take a deep dive into ChatGPT, exploring its architecture, training methodology, and real-world applications. 

By the end, you'll have a solid understanding of the inner workings and potential of this cutting-edge NLP model.

The Architecture of ChatGPT:

ChatGPT is built upon the GPT-3.5 architecture, a variant of the Generative Pre-trained Transformer (GPT) model. GPT-3.5 utilizes a transformer-based neural network, which is composed of multiple layers of self-attention mechanisms. This architecture enables the model to process and generate coherent and contextually relevant responses.

Training Methodology:

Training ChatGPT is an extensive process that involves pre-training and fine-tuning. During pre-training, the model learns from a large corpus of publicly available text data, such as books, articles, and websites. This unsupervised learning phase allows ChatGPT to acquire a broad understanding of human language.

After pre-training, the model undergoes fine-tuning using a more specific dataset that is carefully generated with the help of human reviewers. OpenAI maintains an ongoing feedback loop with reviewers to enhance the model's performance and address any biases or ethical concerns that may arise. This iterative process ensures that ChatGPT continuously improves and provides more reliable and helpful responses.

Capabilities and Limitations:

ChatGPT is capable of a wide range of language-related tasks. It excels at tasks such as text completion, question-answering, translation, summarization, and more. Its ability to understand and generate human-like responses makes it an invaluable tool for various applications, including customer support, content generation, and conversational agents.

However, it's important to note that ChatGPT has certain limitations. It may sometimes produce plausible-sounding but incorrect or nonsensical answers. The model can also be sensitive to input phrasing, where slight changes in the wording of a question may yield different responses. Additionally, ChatGPT's responses are influenced by the data it was trained on, which means it may inadvertently reflect\

or amplify biases present in the training data.

Real-World Applications:

ChatGPT has found practical applications across multiple domains. In customer support, it can handle routine inquiries, freeing up human agents for more complex tasks. Content creators leverage ChatGPT for generating ideas, drafting content, and even producing code snippets. ChatGPT is also valuable for language translation, enabling users to interact seamlessly across language barriers.

Moreover, ChatGPT can serve as a language tutor, providing explanations and examples to help learners grasp complex concepts. It can be used to create virtual assistants, chatbots, and interactive conversational agents, improving user experiences in various applications. The versatility of ChatGPT allows it to adapt to diverse industry requirements.

Ethical Considerations:

As with any advanced language model, there are ethical considerations associated with ChatGPT. The model's responses can sometimes be biased, offensive, or promote misinformation. OpenAI is committed to addressing these concerns by investing in research and engineering to reduce biases and improve the default behavior of the model. Additionally, they provide clear guidelines to human reviewers to ensure responsible and ethical AI practices.

OpenAI also emphasizes user feedback and encourages users to report any problematic outputs to improve the model's behavior continually. Striking a balance between freedom of expression and preventing misuse is an ongoing challenge, and OpenAI actively seeks public input to shape the defaults and hard bounds of the system.


ChatGPT, built upon the GPT-3.5 architecture, represents a significant milestone in NLP and AI research. Its transformer-based architecture and extensive training methodology enable it to generate human-like responses and perform a wide range of language-related tasks. With responsible deployment and continuous improvements, ChatGPT has the potential to revolutionize customer support, content creation, language learning, and more. As we move forward, it is vital to recognize the ethical considerations surrounding AI systems like ChatGPT and strive for a collaborative approach in shaping their development and impact on society.

How did ChatGPT do?

ChatGPT on ChatGPT

The above blog is pretty comprehensive and covers all of the key aspects. It could be argued that more detail should be given to the GPT algorithm and how it works. It could also have gone into more detail about how the iterative learning process is actually performed with both human intervention and automated learning steps. However, it is certainly good enough to be used as the basis of a reasonable ChatGPT blog.

Implications of ChatGPT

This is of course both a potential benefit and a concern – the more we come to rely on an AI system such as ChatGPT to generate such content for us the more we become reliant on the information it provides. This is of course true of any source of information – if we continually read a biased social media site then we may well become influence by that bias. However, the issue here is slightly different in that the system generating the content is not aware of its bias and may be trusted purely because it appears to be impartial.

A further issue is that of plagiarism – how does ChatGPT learn anything – by looking at existing works and analysing them for information and content and then using that information to generate responses to questions. At one level this is ‘stealing from the original authors’ however at another level that is exactly what all of us do anyway. We learn from others via books, web sites, social media, past examples and then use that knowledge to help us solve new problems or generate new content etc. The difference of course is that a system such as ChatGPT can automate this and consume and represent a huge repository of information and currently at least, cannot generate new or novel information.

A further issue can be that of education establishments. There has always been an issue of plagiarism often augmented by essay writing services. Many universities employ software to try and spot plagiarism and often have processes in place to check the authors involvement in submitted assignments (for example by interview or viva). However, ChatGPT has taken this to a new level by being able to generate new content on the fly without the involvement of human agencies and at the click of a button. As an example, we asked ChatGPT to write a Python program to generate a Fibonacci sequence. Here is its response:

User: Write a fibonacci program in python

ChatGPT: Certainly! Here's a concise implementation of the Fibonacci sequence in Python:

def fibonacci(n):
    if n <= 0:
        return []
    elif n == 1:
        return [0]
    elif n == 2:
        return [0, 1]
    fib_sequence = [0, 1]
    for i in range(2, n):
        next_num = fib_sequence[i-1] + fib_sequence[i-2]
    return fib_sequence

# Example usage:
fib_sequence = fibonacci(10)

This program defines a function `fibonacci` that takes an integer `n` as input and returns a list containing the first `n` Fibonacci numbers. The function uses a loop to calculate each Fibonacci number iteratively and appends it to the `fib_sequence` list. Finally, the program prints the Fibonacci sequence for `n=10` as an example.

Not only does the solution have a reasonable Python implementation it also provides an example of using the function and an explanation of the implementation.

I do know that some Universities now have tools to try and identify code generated by tools such as ChatGPT but I am not sure how successfully they will be.

ChatGPT as a Learning tool

Perhaps an interesting aspect to consider is how useful might ChatGPT be as a learning tool. It is certainly possible to ask it questions and for it to generate content that can be used to inform the reader. Of course, there are currently no guarantees as to the accuracy or correctness of those answers. However, unlike a web site or textbook, the questions can eb asked / phrased in ways that the user wants. Thus, a question around a topic can be asked rather than merely looking at canned text in FAQs or reading through paragraphs of text in a book.

In addition, ChatGPT is a chat bot built on top of the GPT algorithm and thus provides a chat like behaviour. For example, following on from the previous question we asked ChatGPT to create a class rather than a function to implement the Fibonacci sequence (perhaps as that is what the instructor had actually requested). This is its response:

User: Could you create a fibonacci class rather than a function

ChatGPT: Certainly! Here's an example of a Fibonacci class in Python that generates the Fibonacci sequence:

class Fibonacci:
    def __init__(self, n):
        self.n = n

    def generate_sequence(self):
        if self.n <= 0:
            return []
        elif self.n == 1:
            return [0]
        elif self.n == 2:
            return [0, 1]

        fib_sequence = [0, 1]
        for i in range(2, self.n):
            next_num = fib_sequence[i-1] + fib_sequence[i-2]

        return fib_sequence

# Example usage:
fib = Fibonacci(10)
fib_sequence = fib.generate_sequence()

In this example, the Fibonacci class is defined with an `__init__` method that takes an integer `n` as input and initializes the `n` attribute. The class also has a `generate_sequence` method that calculates and returns the Fibonacci sequence based on the value of `n`. Finally, the program creates an instance of the Fibonacci class with `n=10`, generates the Fibonacci sequence, and prints it as an example.

With relatively little effort we can interact with ChatGPT and learn about classes, loops etc. asking it to explain each feature.

Is this the same as having a human instructor available who can discuss these concepts directly with a student? We think not – there is a level of passivity in the interaction which requires the student to drive the chat with ChatGPT. In contrast an instructor can listen to the questions being asked and then take the conversation further by exploring associated ideas and structures. However, it may well work well with a static textbook, allowing a user to dynamically raise questions as they proceed.


The future for such technologies is still unclear however it is obvious that tools such as ChatGPT will have a place in many different walks of life. The extent to which we come to rely on them may be a question that needs further consideration but as a quick reference aid, for example to learning, they could be invaluable.

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