What is a Chatbot? Definition, Types and How It Works [2026]
A chatbot is a computer program that simulates human conversations through text or voice, using predefined rules or artificial intelligence. Companies use it to answer frequently asked questions, qualify leads and provide off-hours support without direct human intervention. To understand how this technology fits into a real business strategy, check out our guide on business chatbots.
Chatbot Definition
The word chatbot combines "chat" (conversation) and "bot" (robot). The idea is simple: a program that maintains a dialogue with a person by imitating natural language. But the technology behind it has changed radically in six decades.
The first documented chatbot was ELIZA, created in 1966 by Joseph Weizenbaum at MIT. ELIZA used text patterns and word substitution to simulate a conversation with a psychotherapist. It understood nothing; it only recognized keywords and returned predefined phrases. Even so, many users believed they were talking to a real person.
In the nineties came ALICE (Artificial Linguistic Internet Computer Entity), which introduced the AIML standard to define more complex conversation patterns. It was still a rule-based system, but with a much broader vocabulary.
The qualitative leap came with voice virtual assistants. Apple's Siri (2011), Google Assistant and Amazon's Alexa integrated natural language processing (NLP) to interpret intentions beyond exact keywords. You no longer had to write the exact phrase the system expected.
And then came LLMs (large language models). ChatGPT from OpenAI, launched in late 2022, demonstrated that a chatbot could maintain coherent conversations about any topic, generate creative text and reason about complex problems. Claude from Anthropic and Google Gemini followed the same path. What previously required thousands of manual rules is now solved by a model trained with trillions of parameters. If you want to dive deeper into this technology, we have an article dedicated to LLMs and language models.
How a Chatbot Works
Every chatbot follows a basic four-step flow: the user sends a message, the system processes it, generates a response and returns it. What changes between a simple chatbot and an advanced one is the sophistication of each step.
Processing Flow
- User input: written text, voice message or button selection.
- Processing: this is where everything is decided. A rule-based chatbot looks for exact matches in its decision tree. A chatbot with NLP analyzes the intent and entities of the message. A chatbot with generative AI uses an LLM to understand the complete context of the conversation.
- Response generation: from returning a fixed response associated with a rule to generating dynamic text that adapts to the context.
- Output: the message is sent to the user through the same channel (web, WhatsApp Business, Telegram, etc.).
Key Components
Modern chatbots integrate several components that work together:
- NLP engine: interprets what the user means, detecting intent and entities. Example: in "I want to return my order from Monday", the intent is "return" and the entity is "order from Monday".
- Knowledge base: structured information that the chatbot uses to respond. It can be an FAQ, internal documentation or a vector database for systems with RAG (Retrieval-Augmented Generation).
- Dialogue manager: controls the conversation flow. Decides when to ask, when to respond, when to escalate to a human.
- Integrations: connections with external systems such as CRM, payment gateways or messaging APIs.
The difference between a rule-based chatbot and one with AI is not cosmetic. The first only works with programmed scenarios. The second interprets messages that were never anticipated, which multiplies its resolution rate. To see these differences in action, check out our article on AI chatbots.
5 Types of Chatbots
When you wonder what types of chatbots exist, the answer depends on the underlying technology. Here are the five types ordered by increasing complexity.
| Type | Technology | Example | Best for |
|---|---|---|---|
| Rule-based | Decision trees | FAQ bots | Simple and repetitive queries |
| NLP-based | Natural language processing | Dialogflow | Variable intent, open questions |
| With generative AI | LLMs (GPT, Claude) | ChatGPT | Complex and creative conversations |
| Hybrid | Rules + AI | Intercom | Scaled support with human transfer |
| AI Agent | LLM + tools + memory | GuruSup | Autonomous actions in real systems |
Rule-based chatbots are the simplest. They work with button menus or exact keywords. They're quick to implement and cheap to maintain, but break as soon as the user goes off script.
NLP-based chatbots interpret the intent behind the message. You don't need to write the exact phrase; the system understands variations. Tools like Google's Dialogflow allow you to train intent models without writing code.
Chatbots with generative AI use LLMs to generate dynamic responses. They don't search a list of predefined answers: they generate new text adapted to the context. Their limitation is that they only generate text, they don't execute actions in external systems.
Hybrid chatbots combine rules for predictable flows with AI for unforeseen situations. Platforms like Intercom implement this model, where the bot resolves routine matters and transfers complex ones to a human agent with context.
AI agents represent the evolution of the chatbot. An AI agent doesn't just understand and respond: it accesses external tools (CRM, APIs, databases), maintains memory between sessions and executes real actions. It's the difference between a program that talks and one that solves. If you're interested in creating your own chatbot, understanding these five types is the first step to choosing the right technology.
Chatbot vs AI Agent
The fundamental difference between a chatbot and an AI agent can be summed up in one sentence: the chatbot responds, the agent acts.
A chatbot, even an advanced one with NLP, is designed to maintain a conversation. It can understand complex questions and generate coherent responses, but its capability ends there. If the user needs to cancel an order, check shipping status or calculate a refund, the chatbot transfers to a human.
An AI agent does all that by itself. It accesses the order system, verifies the status, calculates the amount and executes the action. It doesn't need to transfer because it has the tools and autonomy to resolve. Additionally, it maintains memory of previous interactions, so if the customer returns the next day, the agent knows the entire history.
The metrics confirm it: a traditional chatbot resolves between 20 and 40% of queries. A well-configured AI agent reaches 70-85%. For a complete analysis of these differences, you have our dedicated article: AI agent vs chatbot.
Chatbot Use Cases
Chatbots have spread to practically every business department. These are the most consolidated use cases by sector.
Customer service: the original use case. Answering frequently asked questions, managing first-level incidents, confirming order statuses. Platforms like GuruSup deploy customer service chatbots that operate 24/7 on WhatsApp and web.
Sales: automatic lead qualification, contact data collection, demo scheduling. A chatbot on the web responds in seconds versus the hours it takes a salesperson to process a form.
Marketing: distribution of personalized content, subscriber capture, automated surveys and interactive campaigns on messaging channels.
Human resources: resolution of internal queries about payroll, vacations, company policies. Reduces the administrative burden on the HR department for repetitive tasks.
IT helpdesk: first-level ticket management, password resets, basic diagnosis of technical incidents before escalating to the support team.
Frequently Asked Questions
What does chatbot mean in Spanish?
Chatbot translates literally as "conversation robot" or "conversational bot". In practice, the English term is used universally in Spanish. Some media use "virtual assistant" as a synonym, although technically they are not the same: a virtual assistant usually implies voice capabilities, while a chatbot operates mainly by text.
What is the most used chatbot?
In 2026, ChatGPT from OpenAI is the chatbot with the largest user base globally, surpassing 200 million weekly active users. In the business realm, platforms like Intercom, Zendesk and GuruSup lead chatbot implementation for customer support.
Do chatbots use artificial intelligence?
Not all of them. Rule-based chatbots work with decision trees without any AI component. Modern chatbots do use artificial intelligence, specifically NLP to understand messages and LLMs to generate responses. The trend in 2026 is clear: chatbots without AI are relegated to very simple use cases.
GuruSup is the AI chatbot that becomes an autonomous agent for WhatsApp. It responds, reasons and executes real actions in your systems without human intervention. Try GuruSup for free and see the difference between a bot that talks and one that solves.
