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Chatbot vs ChatGPT: Understanding the Differences & Features

Updated on Feb 5
7 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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Although the first chatbot was created in the 60s1, and commercialized by the late 2000’s,2 it’s never been as popular as it is today, thanks to ChatGPT

However, ChatGPT’s distinct success shouldn’t be generalized, because it’s a specific type of chatbot that does not suit all business processes. 

If you want to leverage a text-based conversational AI tool in your business processes, and wish to learn the differences between chatbot vs ChatGPT, this article will explain:

  • What each conversational AI tool is
  • How each works
  • How they differ from each other
  • How to choose between them

What is a chatbot?

A chatbot is a software application that can simulate human conversation. It can chat with a user in different languages and provide instant and consistent responses without human intervention. This flexibility makes them usable across a wide range of use cases and industries.

There are currently three types of chatbots:


No built-in intelligence or learning capabilities. They can’t generate an original response without relying on predefined templates (as generative chatbots do), nor one based on existing parameters (as AI chatbots do).

They receive an input and try to find the closest possible answer in their database. 


AI chatbots leverage ML models to select the most appropriate response from a set of predefined templates and training dataset. Because most AI chatbots are trained on a specific category of datasets, they likely won’t answer questions that’s not in their domain.


Generative chatbots, which include ChatGPT, use a much wider range of data to answer almost any question in any category. This might make them less specialized in any one topic, but they appeal to a larger audience.

How does a chatbot work?

Chatbots generate human like responses to user queries through these steps:

  1. Receiving input: This is a text or voice-based message or command from the user.
  2. Processing input:
    • Tokenization: Input is tokenized into individual words. For example, “How are you?” is tokenized into “How,” “are”, “you”, “?”.
    • Intent understanding: Chatbot tries to understand the user’s intent with natural language processing (NLP) and natural language understanding (NLU). Is it a question, command, or sentiment?
    • Entity recognition: The entity or keywords in the input are identified. For example, in “Book a ticket to Paris”, “Paris” is an entity representing a destination.
  3. Determining the response: Based on the type of chatbot – largely categorized into rule-based, intelligent, and generative – it creates a response. Since we’ll discuss generative chatbots in the next section, we will now focus only on:
    • Rule-based chatbots: These look for answers in their knowledge base that best match the input. Once a match is found, it gives the corresponding predefined response.
    • Intelligent chatbots: AI powered chatbots use artificial intelligence technology, like machine learning or deep learning techniques, to generate or select a response. Instead of looking for exact matches, these bots infer the user’s intent or sentiment.
  4. Returning the response: The best-matched response is finally returned back to the user.

What’s ChatGPT?

ChatGPT is a type of chatbot that uses OpenAI’s generative models to create new responses based on the data it’s been fed with.

Learn about the use cases of ChatGPT.

How does ChatGPT work?

ChatGPT is a large language model trained on the third generation of GPT (Generative Pre-trained Transformer) architecture, with hundreds of billions of words.

Here’s a high level overview of GPT’s functionality:

  • It can generate coherent text sequences
  • It’s pre-trained on large swaths of data to gain general language capabilities, and is then fine-tuned for specific tasks
  • It utilizes the Transformer architecture to process inputs. For example, for the query, “What are some traditional dishes in Italy?,” this is the breakdown:
    • It tokenizes the words
    • It embeds a numerical value and a positional encoder to each word to remember their sequence
    • Gives a weight to each word to focus on the different parts of the input differently (i.e., the word “Give” will have less weight than “recommendation”)
    • It uses layers of Transformer blocks to understand the context. It sees patterns like “traditional dishes in Italy” and infers you’re asking for suggestions on what to eat
    • It generates a response based on the immediate context of what you’ve asked, and its vast training data (i.e., it’s learnt that “pizza” and “pasta” are foods associated with Italy)

What are the differences between chatbot vs ChatGPT?

Rule-based chatbots, AI-chatbots, and generative chatbots like ChatGPT are all conversational agents for automating user interactions. But there are differences amongst them.

  1. Architecture and design:
    • Rule-based chatbots: Have knowledge bases and matching models where they match a keyword with a pre-written answer in the database.
    • AI chatbots: Leverage ML models to create responses based on the specific data it’s trained with. 
    • ChatGPT: An advanced language model, built on the Transformer, that generates new responses based on patterns it’s learnt from vast amounts of data.
  2. Flexibility:
    • Rule-based chatbots: Limited by their knowledge base. If an input can’t fit into the realm of a predefined keyword, chatbot won’t give a relevant answer.
    • AI chatbots: They are moderately flexible, where they can create different kinds of the same answer, but can’t expand beyond their training data. 
    • ChatGPT: Can generate responses to a vast array of questions since they don’t rely on pre-defined templates.
  3. Training:
    • Rule-based chatbots: They aren’t trained like their advanced counterparts, but programmed with predefined rules and responses, in an “if-then” format. 
    • AI chatbots: Trained on specialized datasets tailored to a particular application or domain. Oftentimes, they might need fine-tuning or additional data. Will likely not answer questions outside of their domain. 
    • ChatGPT: Trained on more diverse datasets than AI chatbots, which allows it to have knowledge on a wide range of topics and generalize original data, which is arguably its biggest current appeal to users. 
  4. Conversational depth:
    • Rule-based chatbots: Lacks depth behind offering pre-determined answers, clickable buttons, or connecting to a human agent. 
    • AI chatbots: Offers depth as much as the training data and its ML algorithms allow. For instance, if it’s trained on data about dogs, it’d be able to answer dog-related questions. But if you asked it to name a different mammal besides dogs, it would likely not answer because the only type of mammal it knows is dogs. 
    • ChatGPT: Offers more depth than an AI chatbot and can connect various topics (Figure 1).

Figure 1: ChatGPT connecting laptops to books.

  1. Personalization
    • Rule-based chatbots: Personalization is, at the very best, limited to branching paths.
    • AI chatbots: Can make personalized suggestions in its domain. For example, if a chatbot is trained on music data, it can make personalized suggestions about different genres of music. 
    • ChatGPT: Personalization is extensive. For example, if you mention that you like noir films, and ask it to recommend noir-style songs, it can create a bridge between the two (Figure 2).

Figure 2: ChatGPT making cross-references between different categories.

How to pick between an AI chatbot and a generative chatbot?

You should pick a generative AI chatbot if you: 

  • Need your chatbot to provide unique and dynamic responses, tailored to each query
  • Have a use case that would benefit from creative, human-like responses, instead of something structured and predictable
  • Have the infrastructure to maintain and integrate a complex generative AI model
  • Can handle the higher costs associated with using advanced generative AI models, especially AI-based solutions
  • Are capable of collecting user feedback and fine-tuning the responses the model generates

How to create your own GPT chatbot?

If you don’t want to invest to purchase a chatbot yet, thanks to ChatGPT API, users have found ways to create3 their GPT-powered chatbot on Windows, macOS, or Linux (which we have summarized below). 

Before you follow these steps, note that every user is given $18 worth of credit with their new account. Once you use it all, or don’t use it and it expires, you’ll have to buy it.

  1. Download and install Python4
  2. Run python –version (on Windows) or python3 –version (on mac and Linux) on Terminal
  3. Upgrade Pip, Python’s package installer, on Terminal. Run python -m pip install -U pip (on Windows) or python3 -m pip3 install -U pip3 (on mac and Linux)
  4. On Terminal, run pip install openai (on Windows) or pip3 install openai (on mac and Linux)
  5. On Terminal, run pip install gradio. This will ultimately power the interface of your chatbot.
  6. Download Sublime Text5
  7. Go to OpenAI, and create an account. Then, click on your profile icon, on “View API Keys,” “Create a Secret Key,” and keep the code.
  8. Open Sublime Text and run the following code. Paste your API key instead of “Your API key”
import openai
import gradio as gr

openai.api_key = "Your API key"

messages = [
    {"role": "system", "content": "You are a helpful and kind AI Assistant."},

def chatbot(input):
    if input:
        messages.append({"role": "user", "content": input})
        chat = openai.ChatCompletion.create(
            model="gpt-3.5-turbo", messages=messages
        reply = chat.choices[0].message.content
        messages.append({"role": "assistant", "content": reply})
        return reply

inputs = gr.inputs.Textbox(lines=7, label="Chat with AI")
outputs = gr.outputs.Textbox(label="Reply")

gr.Interface(fn=chatbot, inputs=inputs, outputs=outputs, title="AI Chatbot",
             description="Ask anything you want",

9. Save the document as a new file on your Desktop. Append the name you choose with .py

  1. Go to the downloaded file. On Windows, right click and press “Copy as Path.” On Mac and Linux, just copy the file.
  2. On Terminal, write Python (on Windows) or Python3 (on Mac or Linux), press space, and paste what you copied in the last step.
  3. Copy the “Running on local URL” and paste it into your browser.
  1. You will have your chatbot, built with OpenAI’s Transformer model, leveraging the Gradio user interface.

Figure 3: Gradio’s chatbot UI which we created.

Further reading

If you’d like to invest in a chatbot, explore our data-driven list of chatbot vendors.

And reach out to us with questions:

Find the Right Vendors

This article was originally written by former AIMultiple industry analyst Bardia Eshghi and reviewed by Cem Dilmegani


Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources: Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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