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How do AI Chatbots Work

How Do AI Chatbots Work: Exploring the Basics

AI chatbots are the hot topic on everyone’s lips at the moment, but have you ever wondered how these chatbots work? We will explore the technology behind the AI bots and discuss their great potential but also their limitations and give you a deeper understanding of these potent digital assets. 

digital assistant clippyChatbots have come a long way since the early experiments in the 1960s, and through many trials like Microsoft’s Clippy assistant (which entered with Office 97 and exited with Office XP in 2001). With the developments in AI and Natural Language Processing (NLP), chatbots are becoming more sophisticated to the point where they can now understand and respond authentically to human language. But first, what is an AI chatbot? 

Definition of an AI chatbot  

Simply put, a chatbot is a program that engages in conversations with humans using Artificial Intelligence (AI) technologies such as Natural Language Understanding (NLU) and Machine Learning. Think of an AI chatbot as a virtual assistant that you can talk with in a two-way dialogue. It can understand human language, interpret your questions and respond to them in a meaningful way. Now let’s delve behind the scenes to see how they work. 

How AI Chatbots Work  

AI chatbots work with a combination of technologies that gel together to produce a multi-layered system. 

Natural Language Processing (NLP) 

At a basic level, Natural Language Processing (NLP) is a technology that helps computers understand and process human language. It's used by chatbots and AI programs to understand the words and phrases that people use in a conversation. 

NLP can be broken down into a few different tasks: 

  • Tokenisation: The process of breaking up a sentence into individual words or phrases. 
  • Parsing and part of speech tagging: Understanding the structure of a sentence and the role of each word. For example, figuring out which words are the subject and which are the verb, etc. 
  • Intent Recognition: This discovers the intent behind what a person is saying; what they want or want to do. 
  • Entity Gathering: Identifying and classifying entities like dates, postal codes, amounts, etc. out of a longer text. 
  • Sentiment Analysis: Determining the sentiment expressed by a person during the conversation.  

Together these steps allow the AI to understand the meaning behind the sentences and allowing it to respond properly. The more data it has, and the more advanced the technology is, the better it can understand and generate human language. And an even more exciting field is Machine Learning. 

Machine Learning (ML)  

Machine learning is like a set of rules or instructions that the chatbot follows (the algorithms), to learn from data so it can make decisions without being explicitly programmed to do so. These rules help the chatbot understand the words in a conversation.  

A chatbot also has a way to remember things, and every time the bot has a conversation with someone, it stores the information in its memory to build and grow in its language use. 

As the chatbot talks to more and more people, it begins to understand more words and phrases, and it can respond more accurately. It's the same as when we are learning to speak a new language - the more you practice talking to people, the better you get at it. 

Also, when the AI chatbot makes mistakes or fails to understand something, it uses learns and adjusts for the next time. As a result, the chatbot continuously improves in its understanding of human language. It's like all learning, the more you learn, the more you know, and the better you get. 

There are many different types of machine learning algorithms, but they generally fall into two categories: supervised learning and unsupervised learning. 

Supervised learning: In supervised learning, the algorithm is given a dataset with input-output pairs and learns from these. For example, a supervised learning algorithm might be trained on a dataset of images labelled with the objects that appear in them, with the goal of learning to identify those objects in new images. 

Unsupervised learning: In unsupervised learning, the algorithm is given a dataset without any labels or output variables. The goal is to find patterns or structure in the data, such as grouping similar examples together. For example, an unsupervised learning algorithm might be used to group customers into different segments based on their buying history. 

Although machine learning technology is at a sophisticated level, ML algorithms do have limitations and are not always 100% accurate.  

Neural linguistics in AI 

Neural Linguistics is a field of study that combines Natural Language Processing and neural networks to enable computers to understand and then generate human language. It plays a key role in AI chatbots as it allows them to converse with people in a similar way to how humans would do it. It provides the AI with the tools to understand the context, intent, and sentiment behind what a person says, which is important for producing natural-sounding responses. 

Large Language Models (LLMs) 

Large language models are a type of AI that are trained to understand and generate natural language text. They are based on deep learning techniques, which is a method of training a neural network using a large dataset. 

The basic idea behind an LLM is to give the AI access to a huge dataset of text, for example, books and websites. The AI then uses this data to learn the patterns and relationships between the words and phrases. This process is called "training the model".  

The more data the model is trained on, the more accurate and sophisticated it can become. Also, you can continue to fine-tune it with new data to keep improving the model. 

Custom Language Models (CLMs) 

Generalist Large Language Models like ChatGPT are perfectly suited for broad-based conversations, however, they have one hazardous flaw - they hallucinate about 15%-20% of the time. That is, they simply fabricate facts if they don't know the answer. As you can imagine, this is not acceptable in industry use cases where accuracy is important.

This is where Custom Language Models step up. These models are trained using industry-specific data and they are highly specialised and precise. For example, a CLM used for AI chatbots for the credit and collections industry would need to be trained on millions of real-life customer conversations for it to 'speak the language' of credit and collections. (See: Give AI Chatbots the Edge with Custom Language Models)

how ai chatbots work

Types of AI chatbots

First up, we have the basic rule-based chatbots. These bots are like the diligent students of the chatbot world. They work to a set of strict rules to figure out what to say, and they stick to them unswervingly. These types of chatbots work well for simple tasks and can handle specific questions, but they are limited in how they respond. 

On the other hand, we have the self-learning AI chatbots, which are like the savvy kids in school who are always one step ahead. They use AI to improve their responses over time and they can learn from past conversations and adapt to new situations, which puts them in a class above the rule-based chatbots. They can understand context, intent and also respond to general questions that don’t fit neatly into the decision-tree paths of simpler bots. 

Chatbot architecture is the underlying structure and design of a chatbot, which defines how the chatbot processes text. There are several type, but some of the most common include: 

Rule-based chatbots which stick to the limits of the narrowly defined logical paths.  

Retrieval-based chatbots are like the encyclopedias of the chatbot world. They've got a database of pre-written responses waiting to be used. When someone talks to them, they look for the closest matching response to give back, but if something completely new comes up, they might not know what to say. 

Generative chatbots, are like creative writers. They use neural networks to come up with their own responses on the fly. They're trained on extremely large datasets which makes them able to come up with new answers, but sometimes the answer can be a bit nonsensical if they haven't been trained properly. 

Hybrid chatbots, are like the jack-of-all-trades of the chatbot world. They use a combination of pre-defined rules, pre-defined responses, and a neural network to come up with the best response. They're a blend of both worlds: the structure of rule-based and the creativity of generative. 

Integrating with messaging channels  

AI chatbots can be integrated with various messaging channels so they can interact digitally with customers on the channels they use on an everyday basis, e.g. WhatsApp, SMS and Messenger. Integration typically involves connecting the chatbot to the messaging platform's API, which allows it to receive and send messages via these channels. This use of AI chatbots is taking customer service by storm, especially in contact centres. 

Advantages and limitations of AI chatbots


  • 24/7 availability 
  • Cost-effective  
  • Personalisation  
  • Can handle multiple queries at the same time 
  • Can handle a wide range of questions 
  • Automate repetitive tasks 
  • Natural sounding conversations 


  • Limited capability for understanding emotions 
  • Difficulty in handling complex requests  
  • Reliance on accurate data 

On the downside, AI chatbots can sometimes get things wrong. They're only as good as the data and algorithms they're trained on, so if the data is flawed, the chatbot's responses will be too. They also can't answer every question or handle every situation, so there are still limits to what they can do. 


AI chatbots are getting smarter and more useful all the time. As technology improves, these chatbots are better able to understand human language and respond in ways that are truly helpful. At the moment, they're being used effectively in customer service, as personal digital assistants, and ecommerce. But in the future, they'll be more powerful and will play a bigger role in automation, so people can focus on the more important activities. All things considered, the future of chatbots is looking bright.

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