What is an AI Chatbot? - Definition, principles, GPTs, and building process

The complete guide to AI chatbots from start to finish

Sangsun Moon
What is an AI Chatbot? - Definition, principles, GPTs, and building process

What is a chatbot?

Definition of an AI chatbot

A chatbot is a software application that can converse naturally with humans. AI chatbot is Voice command or text chat, or both, with human conversation via A computer program that simulates. AI chatbot is short for Chatterbot, which has a variety of synonyms, including 'Talkbot' 'interactive agent' or 'Artificial conversation entity'.

AI chatbots are automated program, so the activity itself costs very little. AI chatbots are available to customers throughout the day and aren't limited by time or physical location. This makes them an attractive technology for businesses that don't have the financial resources to have employees working around the clock. Not only can you use your employees' time more efficiently, but you can also save money.

AI chatbot applications

AI chatbots are interactive tools that use artificial intelligence to efficiently perform routine tasks. Chatbots are used to handle simple tasks, especially in business to consumer (B2C) and business to business (B2B) environments.

AI 챗봇은 natural language processing to interact with web services or apps through the user's text, graphics, or voice. You can also use predictive intelligence and analytics to learn about your users' preferences and use this knowledge to make recommendations to them or anticipate their needs.

Generative Pre-trained Transformers (GPT), AI chatbots can understand natural human language, emulate conversations with humans, and execute actions requested by users. Unlike chatbots, which are typically pre-programmed to respond to specific responses, GPTs use machine learning and natural language processing algorithms to generate responses based on the context and tone of the conversation. Language models like ChatGPT, Bard, and PaLM not only answer users' questions, but they can also generate responses to a variety of written Write content.


If you're searching or reading about AI chatbots, you'll see the terms NLP and NLU come up a lot. NLP is an umbrella term for everything related to making AI capable of processing natural language. Natural Language Understanding (NLU) uses various machine learning algorithms to identify emotions and Named Entity Recognition (NER), a subtopic of NLP that deals with semantics.

“Please crack the windows, the car is getting hot.”

Here, NLP focuses on processing the text for its literal meaning. In contrast, NLU will focus on extracting meaning from context and intent. So, given the sentence above, NLP would literally try to break the window. However, NLU can infer that the speaker meant to lower the window.

AI Chatbot: NLU vs. NLP
Understanding Natural Language Understanding

NLP processes text, focusing on grammar, structure, and perspective. NLU, on the other hand, looks behind the language or text to find the It can be said that they specialize in inferring hidden intentions. In the past, chatbots would reset the conversation as soon as it deviated from the format set by the developer, or rephrase the question until it was understood. But by combining NLP and NLU, chatbots can understand the context of the conversation and contextualized meaning.

How to build an AI chatbot?

AI chatbots have become a popular choice for many organizations. The number of builders for AI chatbots has also increased. Let's take a look at what you need to know before you start building your own chatbot.

Development structure and chatbot architecture

Just as there are endless ways to use chatbots, there are also many choices for the basic framework for building them. Let's compare three of them.

AI Chatbot ChatGPT -

Generate AI models

  • Models trained on large datasets for AI chatbots
  • Understand natural language better than traditional methods
  • Examples include ChatGPT, Bard, and other generative AI chatbots developed with large-scale language models.
  • Recently, Google is attempting to generalize these generative models by extending them to its search engine.

Pattern-based heuristics

  • A model that applies a set of rules with patterns.
  • A heuristic is a computer algorithm designed to help a model make quick decisions in situations where there is insufficient time or information to make a rational judgment, or where systematic judgment is not necessary.
  • Heuristics are triggered based on rules and patterns.
  • Mostly used for entertainment chatbots

Intent Classification to Classify User Inputs into Predefined Intents
Intent Classification to Classify User Inputs into Predefined Intents

Intent Classification Algorithms

  • Intent classification is theprocess of identifying the intent behind a user query in a chatbotconversation.
  • User requests can becategorized in many ways, even if they contain common keywords.
  • Classify them according to thetype of business and customer requirements by conducting chatbot intenttraining.
  • A way to respond to cases wherethe context of the request is different even if the same keyword is used.

Korean public data

Korean data has historically lagged behind English. This is because Korean data has an even more challenging structure than English, which is already a complex and demanding natural language to process. In particular, compared to English without investigation, Korean is much more difficult to Tokenization is tricky. This is because words can often be reordered or not spaced to make sense, and AI can't distinguish between meanings by looking at text without punctuation.

Google's new AI chatbot, Bard, can speak Korean sentences naturally. Google has shown its intention to take the lead in the Korean data market, which has been facing technical difficulties. Google's CEO explained, "We will use Korean to see what else we need." In response, Korean companies are also showing their intention to accelerate the construction of "Korean large-scale language models" by claiming that they have more high-quality Korean data.

Back in January of last year, we reported on the artificial intelligence Chatbot Simsim has reported the completion of the construction of a dataset to help Korean conversational AI technology. The dataset includes 'empathic conversation' data, 'knowledge search conversation' data, and 'everyday conversation' data. This paves the way for the creation of a large language model capable of fact-based conversations, persona maintenance, and empathy.

Korean Conversation Data Field Structure
Korean Conversation Data Field Structure

AI HUB is an AI integration platform that enables anyone to utilize and participate in the AI infrastructure required for the development of AI technologies, products, and services. Users can freely utilize infrastructure services such as AI data, AI SW APIs, and computing resources supported by AI HUB. AI 허브에서 Data voucher business, we create data for AI inference learning with public data, R&D data, and private data collected through the program, and share it with various industries and research and development organizations.

Korean data, which is difficult to build, is also available in AI HUB. There is a lot of data for [classification analysis], [similarity determination], [natural language Q&A], [translation], and more, especially for training dialog models.

Challenges to real-world implementation

Chatbots specialize in automating mundane and repetitive processes. Adding artificial intelligence to the mix makes them even more effective. However, if the chatbot is being asked to do more than it is capable of, or if the task it is being asked to perform is more complex than the model is capable of, it is likely to fail.

AI chatbots also use data accessed from a variety of sources. However, if the data is of poor quality, it can lead to Limits the functionality of your chatbot. In other words, the quality of the chatbot is determined by the AI model and data quality that the engineers used in the chatbot.

If so, you can see that building a chatbot requires a more specific design, especially since a poorly designed chatbot can erode user trust and create a bad impression of your brand. Here are some examples of things to consider during the development and build phase of your AI chatbot.

  • The main purpose of an AI chatbot and the Target
  • Where to implement a chatbot?
  • ~Typically implemented on a business website, but can also be built on other platforms such as mobile applications.
  • Define specific chatbot features
  • You can also utilize ~GPT, which can lead to a link like a CTA.
  • ~You can also build in speech recognition for faster online communication.
  • Monitoring and maintenance plans

The reason why you need to design your AI chatbot from the ground up is because there are many more things that users expect from an AI chatbot these days, which we'll cover in the sections below.

AI chatbot interactions

Conversational UX

Conversational user experience (CUX) refers to an approach to interaction based on natural language. When humans interact with each other, they use conversation to convey ideas, concepts, data, and emotional information.

Before the concept of CUX emerged, users had to be willing to learn and adapt to complex guides in order to use a system. But when systems were able to communicate with humans through natural language processing, users were able to understand and use interfaces without much learning. Not only that, but we saw the user experience become more continuous and extended when they had a continuous and contextual conversation with the system. We call this Microsoft says that 'users don't learn the system, they What the system is learning," he explained.

Conversation Design is the Future of UX
Conversation Design is the Future of UX

When designing a chatbot, ensuring a great conversational user experience should be a top priority. A well-crafted conversational chatbot should allow users to solve problems without having to type too much, say too much, repeat themselves multiple times, or explain things to the bot.

However, it is difficult to say that the AI chatbot industry had properly established CUX before ChatGPT. The conversation scenarios of the system were very limited, and the scalability of functions was also very limited. In addition, due to the limited interaction method, there were many short answers without understanding the context, and the communication process was unnatural, causing a lot of inconvenience to users.

Therefore, to compensate for this limitation, it is necessary to have a clear understanding of the interaction types before building a chatbot and implement a proper mix of them.

System-centric type

The agent only recognizes and responds to commands from users that fall into one class. This is the type typically used for web search or voice control, and has the disadvantage that if the user wants to continue the conversation following a response, the system no longer recognizes it. So if the user asks for multiple commands in a row, the system doesn't remember the previous conversation or its context and treats it as a new conversation.

Content-centric types

The content-driven type has a similar pattern to the system-driven type, being limited to sequences of two turns or less. However, it is somewhat different in that it is text-based and provides long, detailed explanations. This type of agent tries to provide a detailed response to the user's question with details and examples. In short, ChatGPT is a typical content-driven type of agent.

Visual Center Type

The visual-centric type, a form of assisted web and mobile interfaces, provides natural language and text-centric interactions. You may have seen graphical-based interactions, such as buttons or lists, with natural language responses. This type creates an intuitive interface by presenting buttons or lists based on categories that formalize predictable user patterns, allowing users to faster to find the desired response. If the command doesn't have what you're looking for, you can also request an action by typing in the text yourself.

Conversation-driven types

Conversational interactions are designed for chatbots to have natural and engaging conversations with users. Conversational chatbots are programmed to be more human-like in their conversational style, meaning they can follow conversational cues while using natural language, and they can tailor their responses to the user's tone and style. They also use things like humor and storytelling to keep users engaged in the conversation. 

Some of the more conversational types try to build rapport and bond with the user, which means they may expand on a topic over multiple turns of conversation based on the user's needs. They may also provide the user with specific, summarized answers instead of providing a list or document. Because they are typically composed of short bursts of conversation, they are often implemented on mobile as well as speakers.

AI chatbot success conditions

Leverage interactions based on user experience design

Recently, many companies have taken on the challenge of creating user-friendly AI chatbots that adopt a conversation-first strategy. However, in some situations, conversations can be more inefficient or frustrating than other interfaces, so the challenge is to mix and match different types to suit your purpose or approach.

Microsoft 365 Copilot Incoming – Next Generation AI
Microsoft 365 Copilot Incoming – Next Generation AI

Microsoft 365's co-pilot pairs a conversational interface with documentation to give users an immediate visual of what they've requested. By weaving together the platform they're working on, the conversation with the chatbot, and the button interaction, it's the most effective use of AI along a continuum of user experience. If the co-pilot had been more conversational and aimed more at creating a bond with the user than solving the requested task, it wouldn't have been as successful.

Training data for AI chatbots is important from the start

To summarize, to build a successful chatbot, you need to mix and match the four interaction patterns and organize scenarios that fit the purpose of the conversation. Specifically, the conversation scenarios here require industry-specific, contextualized data.

Domain knowledge refers to specific information and expertise related to the problem or domain you're trying to model. In order for a chatbot to have a meaningful and informative conversation with a user, there are certain things it needs to know about a particular topic. For example

  • Vocabulary and grammar for your domain
  • Concepts and relationships within a domain
  • Common questions and requests from users about your domain
  • Different ways for users to express their needs and wants

Domain knowledge is the key to ensuring that your chatbot understands the context of the conversation and generates appropriate responses. For example, if your chatbot is designed to perform customer support services, it will need to have a thorough knowledge of your company's products and services. It will also need to be able to understand different types of customer inquiries. It can only perform well if it is trained with data that contains this specific knowledge. This is why understanding and knowledge of your industry, along with the ability to collect and process data, are critical to building a chatbot.

DataHunt AI Chatbot Case Study

Analyze conversation scenarios with granular sentiment analysis

The multimodal sentiment analysis model for psychological counseling includes the ability to detect emotions and analyze the tone and pitch of the counselor's voice to analyze the effectiveness of the counseling session. Prior to this project, Datahunt was provided with textual data and associated audiovisuals, which they used to create a We wanted to understand the context of the conversation and the sentiment of the speaker.

NLP data labeling - comparing video and transcripts of conversations to tag emotions.
NLP data labeling - comparing video and transcripts of conversations to tag emotions.

Workers first watch the video corresponding to the textual dialog, and then compare the video and script of the dialog. Each script can be labeled with Text/video Intent, as well as Topic, Subtopic, and Emotion. We also designed it to be flexible, so that if a line contains multiple emotions that are difficult to categorize into a single emotion, it can be coded into the Emotion 2 column.

With that, DataHunt finished the multimodal transcription of about 79,346 sentences. The accuracy rate was a whopping 99.995% - a result of building sophisticated data by tagging a whopping 8 attribute values, as opposed to the traditional approach of tagging only 2-3 sentiment values.

AI tutors based on data on how teachers talk to students

The SaaS platform that the customer wanted to build was a step-by-step structure of text recognition, translation process, and translation process check. Once the labeler enters the data, the text data is organized for basic science/humanities, object recognition is performed for words, and then foreign language translation is performed for the text. Finally, we added the process of checking the results to improve accuracy.

Building AI Tutors and Learning Platforms with NER

Conclusion: Building an AI chatbot requires prior knowledge and accurate data design

In the future, AI chatbots will combine with existing products or platforms to create more It is expected to provide a personalized agent experience. However, AI chatbots have consistently been criticized for their inherent lack of trust in the information they encounter, especially when dealing with areas such as life and finance, where GPTs based on web information can be fatal. This is one of the reasons why chatbots built on GPTs with a vague interest in AI chatbots may not perform as expected in real-world applications.

Therefore, in any field that requires accurate information delivery, it's important to form a database of trusted domain knowledge and design your work so that inferences and responses can be made based on that knowledge. By defining data beyond language learning, projects that build complex learning data can ensure a connected and trustworthy user experience.

Ultimately, building a successful conversational AI chatbot requires a professional partner with a deep understanding of your business and AI ecosystem. DataHunt is also committed to continuing ChatGPT's vision by providing steady Research and development continues.


  1. An AI chatbot is a software application that can converse naturally with humans. Adopting a chatbot can be a more efficient use of an organization's time and can help your organization provide services during times when live agents are not available.
  2. Chatbots are designed to use natural language processing to interact with a web service or app through the user's text, graphics, or voice. Chatbots can choose an architecture and attraction based on the type of interaction they want to have. The attraction that has received the most attention recently is the conversation-centered architecture, but you should choose the appropriate type based on the intent or purpose of the AI chatbot you're building.
  3. For AI chatbots to be successful, conversation scenarios that capture user intent and context must be developed and reflected in the training data. Not every AI chatbot based on GPT can deliver the expected performance. Therefore, a complex project design based on domain knowledge is required when building language training data.


  1. Conversational user experience - Bot Framework Composer | Microsoft Learn
  2. Exploring Generative AI in conversational experiences: An Introduction with Amazon Lex, Langchain, and SageMaker Jumpstart
  3. Conversation Design is the Future of UX | by Greg Bennett | Salesforce Designer | Medium
  4. NLP vs. NLU: from Understanding a Language to Its Processing - KDnuggets
  5. Chatbot Architecture | Engati

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