Chatbot: What it is, how it works, what it is for and types of chatbots

 It does not matter if you are an expert in the field or not, but surely you have heard of chatbots. You know what one of the buzzwords is, but you may not know what they are or what specific benefits they can bring to your company, that's why it's important that you read this post.

Next, we will answer basic questions such as: what is ithow does a chatbot work, what is it for, and the typology according to its intelligence, interaction, or channel.


A chatbot is a chat robot that communicates with users through text messages using artificial intelligence. It is software that is integrated into applications, and websites... in order to resolve user doubts at any time without the need for a physical person behind it, becoming the ideal virtual companion.

They are normally designed to communicate with real people, however, applications are being developed in which two chatbots can communicate with each other.

In short, it is a tool that interacts with users automatically, directing them towards the action desired by the company.

This would not be possible without the implementation of machine learning algorithms developed by AI (Artificial Intelligence), which allow the program to interpret or intuit the user's tastes, preferences, or even habits. So much so, that sometimes these communication bots can adopt such extreme realism in communication with users that it is difficult to discern whether we are really communicating with a robot or a person.

But... What is behind a chatbot? Can you really teach a machine to learn? Can a machine have feelings or understand people's feelings? Without going too deep and in a simple way, we will try to explain it.


First of all stay with three concepts: NLPNLC, and NLGCalm down, although it may seem so, these are not the words that should be recited to conjure up a satanic ritual.

Natural Language Processing (PLN) / Natural Language Processing (NLP): 

Natural language processing is used to classify unstructured texts into a set of topics. In other words, it is the field of AI that is responsible for investigating how machines communicate with people through natural languages. 

The software will try to break down the content issued by the user to facilitate its understanding. Differentiate words and sentences in the text. Correcting spelling errors before determining their meaning to facilitate their understanding or even considering the user's emotions would be another factor in this stage.  

Natural Language Understanding (CLN) / Natural Language Understanding (NLU):

Nobody is surprised today that a person is able to read a message, interpret it and understand it in a given context or situation, right? However, if we go back to prehistory, the first hominids were not capable. A process of evolution has been necessary to achieve this. And that is the goal. With NLP, a machine is capable of processing a large amount of data, but is it really capable of understanding it?

Let's imagine that we want to search for a document, in a search engine that connects to a database and we write a sequence of letters registered in the database. It doesn't matter if this sequence of letters has a meaning in our language or not, the search engine will recognize it and open the document.

Now suppose that we write in the search engine ideas or concepts related to the content of the document, without the need for these to have been formulated with the words contained in it.

While the first document browser would NOT be applying NLU, the second example would be a great application of Natural Language Understanding since it implies that the software has been able to understand and interpret the idea raised by the user.

This is one of the big problems to solve when determining whether or not a machine has Artificial Intelligence and is known as intent classification.

For this to be possible, that is, to know if the machine is really capable of recognizing our intention or if it is limited to following a sequence of instructions, the machine is trained with an automatic learning algorithm, subjecting it to tons of training data that they understand the user's messages, their intentions and the possible variations of messages with the same intention.

Natural Language Generation (GLN) / Natural Language Generation (NLG)

Natural Language Generation is part of Natural Language Processing and naturally has certain aspects in common. Somehow both have different but complementary focuses. 

While the NLP seeks to identify analytical knowledge from textual data, the NLG combines analytical knowledge with synthetic text to create narratives within a context. That is, transform structured data into a written narrative. 

And how is this achieved? Well, through a sequence of steps to follow:

  1. Content analysis: Identification of the main themes of the source document and the relationships between them.
  2. Understanding of the data: Interpretation of the data, identification of patterns and contextualization of the same. It is often at this stage that machine learning is integrated into the software.
  3. Documentary structuring: Planning of the document and choice of a narrative structure based on the type of data to be interpreted. 
  4. Sentence aggregation: Sentences or relevant parts of sentences are combined in a way that appropriately summarizes the topic.
  5. Grammatical structuring: Grammatical rules for text generation are integrated. The program deduces and interprets the syntactic structure of the sentence to finally rewrite said information in a grammatically correct way.
  6. The language: The final output is generated based on the template that the user or programmer has selected. 


  • Solve doubts about products or services
  • Send emails to customers
  • 24h personalized attention

In short, a chatbot implies a series of advantages or benefits for the company such as: increasing work efficiency, generating leads, reducing costs for the company, improving sales strategy and/or reducing interaction times with customers.


In this section, we will clarify the different types of chatbots in terms of their intelligence, interaction or channel.

Chatbots according to their intelligence: 

Based on their intelligence, we are going to comment on the types of existing chatbots so that you can evaluate which one best suits the needs of your company if you are considering integrating one on your website or other platforms.

Dumb Chatbot:

The most basic. Also known as " ITR Chatbot "It is software that follows simple commands previously implemented by the programmer following a sequential logic without the need to use AI.

They emulate the conversation, however, they are not capable of interpreting a response from the user but are limited to offering various options and opening others depending on the option selected.

Chatbot with Word-Spotting technology:

Imagine as a company that you want your chatbot to be able to recognize important keywords for your business or intention. These types of chatbots do not use artificial intelligence either, but they are capable of giving a response based on previously configured keywords throughout the interaction with the user. 

However, often the chatbot's response, despite including the keywords in the response offered, is not precisely tailored to the needs or questions raised by the user, so on more than one occasion, they generate friction in the chatbot. the communication.

Chatbot with artificial intelligence:

Crown jewel. These charming bots are able to learn, understand the user, and interpret their needs. You remember the fantasy car, right? Well, this is something similar thanks to the artificial intelligence, machine learning, and natural language processing that we have previously talked about. We are not going to delve into these terms in this post, all you need to know is that thanks to these technologies, the bot acquires the ability to understand and interpret, achieve a more natural conversation with users, and solve doubts or problems. They also work very well as conversational sales platform assistants accompanying the customer throughout the purchase process.

Chatbots according to the type of interaction: 

Text chatbot:

Elementary functions limit interaction with users to the level of text messaging.

Dynamic chatbot:

They simulate a more real interaction by combining text messaging with photos, videos, or GIFs through the previously selected platform.

Voice chatbot:

I'm aware that it's not going to be the most technical definition you've read online, but if I ask you about Siri or Alexa, you know what I'm talking about, right? Well, voice chatbots, with a greater or lesser degree of intelligence, are basically just that.

Chatbots according to the channel:

Finally, we could segment the type of chatbots according to the channel where they are integrated as follows.

Website chatbot:

Perhaps the best known. It offers 24-hour assistance to users and is responsible for generating leads. Also applicable to e-commerce to accompany the customer throughout the purchase process to make it easier and more comfortable, improving the user experience.

Social media chatbot and instant messaging:

It could make a difference between the two but basically, the purpose is the same. Its objective is to ensure user engagement and answer their questions at any time. It's about getting to where your potential customers are already and eliminating friction by avoiding waiting times.

Omnichannel chatbot: 

Today there are chatbots that can be implemented on any channel. Here the important thing is that the company knows how to adapt the need or purpose of the chatbot to the channel, since a bot that tries to capture leads is not the same as one that provides customer service support.

And little more to say about these technological wonders. If you liked the content, you may be interested in other related posts. Leave us your comment with any questions, curiosity, or suggestions for new posts.

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