The first chatbot was introduced in the 60s before becoming commercialized in the latter part of 2000; however, it didn’t reach the popularity of ChatGPT today due to its popularity. But its popularity should not be interpreted as general, since it’s a certain type of chatbot that isn’t appropriate for all business processes.
If you are looking to use the power of text-based conversations as an AI tool to improve your work processes and would like to know the distinctions between ChatGPT and traditional chatbots, you should know the ways they differ from one another and what you can do to select from them.
Table of Contents
What are chatbots?
Chatbots are software that mimic human conversation using text, using technology like NLU or NLP. AI chatbots use technology like Natural Language Processing and machine learning to comprehend users’ intent to analyze data, then provide precise responses.
They can chat with users in various languages and offer rapid and consistent responses with no human intervention. Artificially-generated chatbots can learn from interactions with users and change with time, enhancing their ability to process complex questions and deliver personalised interactions. This makes them useful in a myriad of industries and use cases
There are three different types of chatbots:
Chatbots based on rules
There is no built-in intelligence or any ability to learn. They cannot create a unique response based on predefined templates (as chatbots that generate their own responses do) or that is based on parameters already in place (as AI chatbots do).
Chatbots based on rules are the most basic type. They function by comparing the inputs of a user to a set of predetermined responses. Consider the flowchart as transformed into an actual conversation.
How they function How they work: When you enter “I want to return an item,” the chatbot looks through its database for the phrase or other similar keywords. If it comes across an appropriate match, it responds by displaying an exchange policy. If it fails to find any match, it requires you to change your words or connect you with an individual.
Strengths:
- Giving the same answers over and over again
- Compliance requirements must be met.
Limitations:
- They aren’t able to comprehend the variations in the way people ask questions.
- They don’t learn or grow by themselves.
- Users are frustrated when their questions don’t follow the expected patterns
AI-Powered Chatbots
The chatbots make use of machine learning to determine the needs of users. They’re trained to understand specific areas or sectors.
What they do instead of searching for keywords, chatbots look at the intention behind an email. They know the message that “I want to return this,” “How do I send this back?” and “Can I get a refund?” They all refer to the same thing. They choose the most appropriate answer from the training information.
Strengths:
- Different ways to ask the same question.
- The quality is improving as more and more people make use of these devices
- Costs moderate ($500-3,000 each month)
- Expertise in their particular subject
Limitations:
- They’re not able to answer any questions outside of the scope of their training
- They will require regular training, and they need it every couple of months.
- Responses are selected from templates and are not designed from scratch.
Generative chatbots
Generative chatbots, such as ChatGPT, can use a larger array of data to answer virtually any inquiry in any area. ChatGPT is a ChatGPT chatbot is a sophisticated conversations using AI technology to offer more natural and human-like conversations. This could make them less adept in one area, but they will appeal to a broader public.
Strengths:
- Answering questions about almost all subjects
- Generating nuanced, contextual responses
- Work with images and text
- The lessons learned from every conversation
- The ability to reason across different topics
Limitations:
- Higher costs ($1,000-10,000plus plus per month, based on the usage)
- Sometimes, they make up information that appears to be correct but isn’t.
- Needs constant monitoring
- Responses may be incongruous
How Smart Are They Really?
One of the biggest differences is the degree to which each can solve issues. This is what each type can do.
Level 1: Pattern Matching (Rule-Based)
They simply recognize the words. If you search for “refund,” they give you the policy on refunds. If you type “I want my money back,” they may not be able to comprehend because these exact words aren’t included in their database.
Level 2: Understanding Intent (AI Chatbots)
They know the concept that “refund,” “money back,” and “return payment” all mean the same idea. However, they aren’t able to combine different ideas or make sense of different subjects.
Level 3: Following Context (Basic Generative AI)
These are the things you’ve talked about earlier. When you inquire, “What’s your return policy?” and “How long does the refund take?” It recognizes that you’re still talking about returns.
Level 4: Multi-Step Thinking (Advanced Generative AI)
They can logically link information. Example: “I bought item X three months ago. The policy states 90 days. Do I have to exchange it?” The chatbot calculates the timeframe, reads the policy, and then gives you a response.
Level 5: Connecting Different Topics (Frontier Generative AI)
These may pull data from different fields. For instance: “Compare your product to a competitor, considering industry trends and my previous purchases.” It is a combination of the information from your product, along with market analysis, as well as your past purchases.
What does this mean to you: Most companies require only level 2 or 3 capabilities. Five and 4 levels have high costs and are more complex. Do not pay more than you actually need.
What Works Best for Different Industries
E-Commerce
The best option is a mixed approach (rule-based for checkout and AI for questions about products)
About 70% of online shopping queries are transactional, such as order status and returns. Chatbots based on rules handle these issues flawlessly. The remaining 30% of questions are related to product issues, which is where AI is a real asset. The majority of e-commerce businesses expect a return on investment within 6 to 12 months.
SaaS and Tech Support
The best choice is an AI-powered chatbot
Technical questions come with a variety of possibilities, yet they remain within the product’s capabilities. An AI chatbot trained to your product will reduce support ticket costs by 40% to 60%. A typical ROI is between 8 and 14 months.
Healthcare
The best choice is rule-based compliance, and AI to provide general data
Healthcare is governed by strict rules. It is essential to have auditable and consistently formulated responses. It is not feasible to pay for a chatbot that is generating medical data. The majority of healthcare facilities report an ROI of 12-18 months.
Financial Services
The best choice is AI-powered, with strict security controls
Financial questions are complicated, but should be within the boundaries of regulation. This requires extensive testing and supervision. The typical ROI is between 14 and 20 months.
Content and Media
The best choice is Generative AI.
Your clients ask various, innovative questions, and they expect sophisticated responses. Generative AI works well here. ROI typically occurs in 10-16 months.
Available Platforms
Rule-Based Options
The Tidio Company, Chatbot.com, ManyChat: $50-200 a month. Great for small-sized businesses and basic automation.
AI-Powered Options
Zendesk, Intercom, Drift: $500-2,000 per month
Ada, Ultimate.ai: $2,000-5,000 per month. Suitable for mid-sized firms with distinct support categories.
Generative AI Options
OpenAI API (GPT-4): $0.03-0.12 per 1,000 tokens (cost is dependent on the use)
AthropicClaude $0.015-0.075 for 1,000 tokens
Special Enterprise Solutions for Customization: $5,000 to $50,000 per month. Ideal for large businesses and more complex use cases.
Hybrid Solutions
Kore.ai, Yellow.ai: $3,000-10,000 per month
Custom Integration: Different Ideal for companies that want to optimize cost and performance.
What is the process of a chatbot?
Chatbots are software programs that communicate with humans using human-like interactions. They follow the following guidelines when making this happen:
- User input: It is a voice or text message, or a command given by the user.
- Processing input
- Tokenization: The input is converted into individual words. For instance, “How are you?” is tokenized into “How,” “are”, “you”, “?”.
- Understanding intents: Chatbots strive to discern the user’s intentions by using natural processing of language (NLP) along with natural language comprehension (NLU). They determine if the query is a query, a command, or a sentiment.
- Entity recognition Keyword recognition: The keywords or the entity that are in the input are recognized. For instance, the input “Book a ticket to Paris”, “Paris” is an entity that represents a destination.
- 33Deciding on the type of response: A chatbot is able to generate appropriate responses based on its type. In the following section, we will solely focus on chatbots that are generative. For more information on chatbots, read the article about chatbot types.
- Returning the answer: The best-matched response is then given to the person who requested it.
What’s ChatGPT (Generative Pre-trained Transformer)?
ChatGPT is a chatbot-based interface that is built on OpenAI’s generation models. The technology behind ChatGPT is its Transformer structure, which permits the program to analyze and create human-like texts.
In contrast to systems that rely on rules, ChatGPT generates responses by anticipating what content will be next using patterns derived from large amounts of written material. This method lets the system handle issues and topics that weren’t specifically programmatically incorporated into the system. Users can ask questions about the past, request help with creative writing, ask for help with programming, or engage in discussion that is open-ended on nearly every subject.
ChatGPT doesn’t just search for keywords to generate predetermined responses. It analyzes the context of conversations and creates unique responses to each interaction. So, two people asking similar questions could get different responses based on the manner in which they formulate their inquiries and what was talked about in earlier conversations.
What is the process behind ChatGPT’s operation?
ChatGPT is a huge language model that was trained using the 3rd generation GPT (Generative pre-trained transformer) architecture, and contains millions of words.
Here’s a quick outline of GPT’s functions:
- It can produce coherent text sequences
- It’s trained on vast swaths of data to acquire general capabilities in language. It is then refined to perform a specific task.
- It uses the Transformer technology for processing inputs. For instance, for the query “What are some traditional dishes in Italy?” Here is the way to answer:
- It symbolises the words
- It adds a numerical value and a positional encoder for every word, allowing them to be remembered in the sequence.
- Gives weight to each word to concentrate on different aspects of the input differently (i.e.”Give” will have less weight than “Give” will have less importance in comparison to “recommendation”)
- It makes use of multiple layers of Transformer blocks to analyze the context. It recognizes patterns like “traditional dishes in Italy” and then infers that you’re asking for recommendations on what you should consume.
- It produces a response in response to its immediate surroundings of the question you’ve requested, as well as its extensive learning data (i.e, it’s been taught it’s true that “pizza” and “pasta” are two foods that are commonly associated with Italy)
What are the main differences between chatbots that are traditional chatbots and ChatGPT?
Artificial Intelligence-driven and generative chatbots, such as ChatGPT, are chatbots based on conversation that can automate user interaction. However, there are some differences between these chatbots.
Design and architecture
- AI chatbots: Make use of ML models to generate responses that are based on the information they’ve been trained on.
- ChatGPT is a sophisticated language model, based on the Transformer, that creates new responses based on patterns learned from huge quantities of data.
Flexibility
- AI chatbots are surprisingly flexible. They can come up with different versions of answers to the same question; however, they aren’t able to expand beyond the training data.
- ChatGPT can provide answers to numerous questions, since they don’t use pre-defined templates.
Training
- AI chatbots have been trained using special datasets that are tailored to specific domains or applications. They might require tweaking or other information. They are unlikely to respond to questions outside of their field of expertise. AI chatbots can provide depth that is determined by their training data, along with their machine learning algorithms.
- In the case of a dog, for instance, they are trained on dog-related data and dogs, and they might be able to answer questions relating to dogs. If you ask it to identify a different mammal, but it wouldn’t answer because the only type of mammal it recognizes is dogs.
- ChatGPT is trained using different datasets in comparison to other AI chatbots, allowing it to have knowledge of an array of subjects and also generalize the original data. This feature is perhaps the biggest draw for users. ChatGPT provides more in-depth information than conventional AI chatbots and is able to connect different topics efficiently.
Multimodality
- AI chatbots might possess advanced text-based capabilities; however, they are generally not multimodal and are limited to monomodal interactions.
- ChatGPT’s multimodal capabilities permit ChatGPT to handle and create responses from text as well as images. This allows for flexible applications like caption writing, G-code generation, and the creation of alt text.
Personalization
- AI chatbots can provide individual suggestions in their area of expertise. For instance, if the chatbot is trained based on music data, it could offer specific recommendations on various genres of music.
- ChatGPT’s personalization capabilities are extensive. For instance, if you say that you like noir movies and request it to suggest songs that are noir-inspired and movies, it could build a bridge to both.
Reasoning
Reasoning models are classified by their complexity and the ability to deal with context and abstraction.
- There is no reasoning; responses are simply static and reactive.
- Chatbots: Rules-based chatbots function at this level and respond to predefined words.
- ChatGPT does not depend on the old logic; rather, it relies on dynamic inference to analyze the context.
- O1 reasoning: Direct, linear reasoning with single-step logic.
- Chatbots are limited. AI chatbots make use of this reasoning to answer simple questions.
- ChatGPT: Using o1 reasoning, but it goes further than that.
- O2 reasoning: limited multi-condition reasoning that is slightly expanded in context.
- Chatbots are a type of chatbot that employs O2 reasoning to complete tasks like answering “If my order is delayed, can I request a refund?”
- ChatGPT easily handles O2 reasoning and solves multi-condition queries, like analysing workflow dependencies and user-specific situations.
- o3 reasoning: Multi-step, or layers of reasoning that connect data across various conditions.
- Chatbots are rarely able to function at this level because of constraints on logic and retention of context.
- ChatGPT operates in O3 reasoning, establishing connections as well as synthesizing multiple-step logic.
- O4 reasoning: Thinking across multiple dimensions or synthesizing a variety of inputs.
- Chatbots: A majority of chatbots can’t do this kind of reasoning because they are unable to integrate different information or handle confusion.
- ChatGPT: Uses O4 reasoning to handle complex, multi-domain projects. For example, if you want to respond to “Compare renewable energy policies in the U.S. and Germany and explain their impact on global carbon emissions.”
- reasoning: Meta-reasoning, in which systems analyze their reasoning process or investigate alternatives to solve the problem.
- Chatbots: No rule-based, basic AI chatbots can function at this point, since they require self-reflection as well as adaptive learning.
- ChatGPT: It can approximate 5 logic by measuring its trustworthiness in its responses or soliciting clarification from users.
How do you choose between an old-fashioned AI chatbot and a machine-generated chatbot?
It is best to select a traditional AI Chatbot when:
- Do you require repetitive tasks or frequent user inquiries, like questions about appointment schedules or tracking orders?
- Choose consistent, scripted responses, not spontaneous or imaginative responses, in order to guarantee compliance, especially in cases where the effect of not having subtle nuance or context is negligible and the scripted responses are sufficient.
- You may have a tight budget or resources and are looking for an affordable, simple solution to install and keep.
- You have a system that can’t handle the demands of complex AI models, and you require an easy-to-use chatbot that can integrate effortlessly into your current systems.
- You want total control over every interaction with the user to reduce uncertainty and reduce the need to monitor models continuously or tune.
It is recommended to choose an automated chatbot if:
- Do you require your chatbot to give specific and dynamic responses specific to the particular query
- Use case that could benefit from the use of creative human-like, human-like reactions, instead of something standardized and predictable
- Are you able to maintain the infrastructure and incorporate a complex AI model that is generative?
- Are they able to manage the higher cost that comes with the use of advanced artificial intelligence (AI) models? AI models, particularly AI-based solutions
- It can be used to collect user feedback and fine-tune the results that the model generates.
The creation of your own chatbot permits the possibility of personalization and customization that will meet the needs of your company. This method improves efficiency in customer support, as well as sales automation, and assures the reliability and accuracy of the responses offered by these sophisticated systems.
How can you build your own chatbot powered by GPT?
If you’re not yet ready to invest in a chatbot, you can make yourself a GPT-powered chatbot by using ChatGPT’s API for Windows, macOS, or Linux. Here’s how:
A variety of AI tools, including Python, OpenAI library, and Gradio, are necessary in the creation and deployment of a GPT chatbot.
Each account is credited with a credit of $5. If that credit is exhausted and expires, then you’ll have to purchase additional credit.
- Install and download Python.
- Make sure you are running the Python version by running python -version Windows or python3 -version for macOS or Linux.
- Upgrade Pip Python’s package installer. You can run python –m pip install -U pyp on Windows or python3-m pip3 install -U pip3 for macOS as well as Linux.
- Installation of OpenAI. Install the OpenAI library. Use pip install openai for Windows, as well as pip3 to install the OpenAI library for macOS as well as Linux.
- Install Gradio by executing pip install Gradio. This will configure the chatbot’s interface.
- Download Sublime Text.
- Set up the OpenAI account. Visit “View API Keys,” select “Create a Secret Key,” and then copy the key.
- Start Sublime Text, enter the following code, and then replace “Your API key” with the key you copied.
