A chatbot is an automated computer program capable of simulating human conversation. Using artificial intelligence, chatbots can understand what a human user says and respond in a fluent and cogent way. This makes them particularly useful as customer support representatives and virtual assistants.
Chatbots can be simple interfaces that answer only specific questions with single-line responses, or sophisticated programs that evolve and deliver increasing levels of personalization the more they gather and process information.
Chatbot Definition
A chatbot is an automated computer program that can simulate human conversation. Using artificial intelligence (AI), chatbots can understand what a human user says and respond to them in a coherent way.
No matter the format or size of the chatbot, “the goal is to get the customer to self-serve,” Maria Aretoulaki, the head of voice and conversational AI product development company GlobalLogic, told Built In. Chatbots make it easier for users to find the information they need by automatically providing responses to their requests or questions — be it audio or text — without the need for human intervention.
How Do Chatbots Work?
Chatbots rely on machine learning and deep learning — components of AI with some nuanced differences — to develop a more granular knowledge and continue learning as they’re exposed to more data in the form of human language. This improves their ability to anticipate users’ needs and formulate the correct response over time.
“The best chatbots are ones that can understand your intent and do an action,” Rob LoCascio, the founder and CEO of conversational AI company LivePerson, told Built In. “That’s really the yin and yang of a chatbot. It’s the intent — what do I want? And then the action.”
Some more sophisticated chatbots are powered by a neural network, which is a mathematical system that learns skills based on the patterns and relationships it finds in large quantities of digital data. Neural networks are good at a lot of things, including mimicking human language in what are called large language models.
AI companies are rolling out neural-network-powered chatbots that can carry out real-time conversations with humans. These are what former Google software engineer Daniel De Freitas calls “open-ended” chatbots, meaning that they can talk about any subject.
“Open-ended means everything goes,” De Freitas told Built In. These chatbots are good at an “uncountable number of things,” thanks largely to an immense amount of both training data and computing power.
De Freitas created one of the very first of these kinds of chatbots, LaMDA, which has since been followed up by large language models like ChatGPT, Bard, Bing Chat and others. He now heads a company called Character.AI, whose open-ended chatbot has garnered the financial backing of major VC firms like Andreessen Horowitz.
Chatbots vs. Conversational AI
Some chatbots are a subset of conversational AI, a broad form of artificial intelligence that enables a dialogue between people and computers. These conversational AI chatbots use artificial intelligence to replicate human dialogue and can handle everything from open-ended questions to super specific requests.
Other chatbots don’t use artificial intelligence at all. Instead, they rely on a series of pre-set answers that only work for a limited set of predetermined statements and questions.
Types of Chatbots
There are many different types of chatbots — some are limited to just a menu of questions and answers, some recognize keywords, and some communicate via voice inputs and outputs. But they can all be placed into one of two buckets: Rules-based and predictive.
Menu-Based Chatbots
Menu-based chatbots are one of the least sophisticated types of chatbot systems. They operate on a decision tree system and guide the user through predefined choices until they reach a desired answer. Creating a menu-based chatbot requires structured options instead of natural language processing systems in more advanced chatbots. Because of their easier to use interface, menu-based chatbots are popular customer service tools.
Rules-Based Chatbots
Rules-based chatbots hold structured conversations with users, similar to interactive FAQs. They can handle common questions about a particular product or service, pricing, store hours and more. They can also handle simple, repetitive transactions such as asking customers for their feedback or logging a request.
Rules-based chatbots are commonly used in more customer service-oriented tasks. They’re also useful in internal business operations since they can handle repetitive jobs such as onboarding new employees or answering questions on specific company policies.
Predictive Chatbots
Predictive chatbots are capable of sophisticated and nuanced conversations thanks to its use of natural language processing, natural language generation and other elements of AI. They’re good at understanding context, and can anticipate what a user might need next.
Some of these chatbots are more open-ended, like De Freitas’ Character.AI; or Kuki, which has managed to beat the Loebner Prize Turing Test, an annual competition to determine the world’s most human-like chatbot, five times. Others are more task-oriented and limited in scope.
“Chatbots kind of run the spectrum,” Joseph Gallagher, the VP of product at mental health-focused conversational AI startup Woebot Health, told Built In. “There are some chatbots out there now that are conversational in the absence of a goal. They’re just there to let people chit-chat,” he continued. Some are “very, very transactional,” and others are “conversational with an overarching goal.”
Voice Chatbots
Voice chatbots like Siri and Alexa interact with users through spoken language.They rely on a combination of speech recognition, natural language processing and speech synthesis to understand and respond to voice commands. First they use a speech-to-text algorithm to convert audio input into text, then they process the query and generate a relevant response. Depending on the specific platform, the reply may be delivered as text or spoken back using text-to-speech. Voice chatbots offer user-friendly interfaces and are widely used for personal assistance, customer service and automating routine tasks.
Benefits of Chatbots
Whether it’s to improve customer experience or boost operational efficiency, chatbots are quite useful, and they offer a variety of benefits for both businesses and individual users.
Make It Easy for Humans to Ask Questions
The ability to foster this feeling of personal relationship is perhaps one of the biggest, most profound benefits of chatbots.
“Chatbots, if they’re good, allow us to have a conversation,” LoCascio said. “The chatbot is basically just reinforcing personalization in a person’s brain that this thing is listening to me, it understands me, and is responding to me. And I think that’s the greatest benefit to this technology, is that it feels very personal.”
At the same time, chatbots don’t judge, which can also be important. So, they provide the personal connection people want, without the judgment that can come with talking to people — particularly when it is a sensitive subject like mental health, or healthcare-related questions.
Boost Operational Efficiency
Even the less sophisticated chatbots that aren’t capable of complex conversations are able to automate a lot of the rote or mundane tasks that humans don’t necessarily need to be doing.
For example, an e-commerce company may want to have a chatbot on its website to answer users’ questions about specific products or services. Or an HR department at a company may want to implement a chatbot so that employees have 24/7 access to information about benefits and company policies — all without having to have a human on call.
That said, Aretoulaki of GlobalLogic says it is important to have humans involved in the design and training of a chatbot — both so that the tool actually serves its purpose, and so that it is built ethically and with as little bias as possible.
Improve Brand Recognition and Loyalty
Beyond these more practical benefits, chatbots have the long-term potential of improving customer engagement, and even brand recognition and loyalty. Going forward, Gallagher expects that the more branded chatbots come on the scene, the more people’s relationships with those brands will be dictated by that chatbot. The way a particular brand’s chatbot communicates — the language it uses, its tone — will become a part of a brand’s reputation with consumers.
“A lot of the people who are using, or proposing to use, this technology have existing businesses. The question isn’t so much about consumers’ relationship to this technology, it’s about consumers’ relationship to companies who use this technology. How does that change?” Gallagher said.
Challenges of Chatbots
Chatbots are by no means a perfect piece of technology, and they still come with plenty of challenges.
Require Lots of Training Data
For many, the biggest challenge of having a chatbot occurs before the chatbot is even built: Acquiring enough training data.
Chatbots often require vast amounts of data to ensure accuracy. Large companies like Google, Microsoft and OpenAI have virtually unlimited computing power, and are capable of tapping into unlimited volumes of data across the web. But most other businesses are much more constrained. Their proprietary data on customers and the business — which are necessary if they want the chatbot to offer accurate answers — is not accessible online. Rather, it resides in corporate data centers, in different formats. Using it effectively looks more like an archaeological excavation than a broad sweep of the internet.
Can Be Difficult to Choose the Right One
Figuring out exactly what kind of chatbot a business should make can also be challenging.
“Part of the chatbot design should be to determine whether you need one, which type you should use, for which tasks, what you should automate, and where and when,” Aretoulaki said. “How sophisticated do you want it to be? Should it only be predetermined options where you press buttons? Or will you let the customer express themselves in a free-text form? This of course is more tricky and therefore more dangerous if you don’t understand it.”
Have Quality Issues
Once a chatbot is made, it is far from perfect. For one, chatbots (particularly those that use generative AI to form responses) get things wrong all the time. They can fabricate information, and format it in a way that is so eloquent that it is difficult to spot.
Chatbots also remain fairly unintelligent — meaning that, despite ongoing fears, chatbots cannot fully replace human jobs (yet). Although the technology has come a long way, chatbots are not sentient or conscious. They don’t understand the complexities of life, or what it means to be human. And at times this can be apparent in the quality of its outputs
“[This technology] is not there yet, in terms of understanding how to be human. It doesn’t have our je ne sais quoi. Whatever we have as humans that makes us unique and interesting,” Jason Gilbert, a multi-modal designer at Intuition Robotics told Built In. “I don’t think this thing will replace us. I think it’ll make us work differently. It’ll give us the tools to work differently, to work more efficiently — at higher outcomes and higher outputs.”
Still, as with all of artificial intelligence, chatbots are continuing to evolve fast. And with the release of ChatGPT’s API, along with the falling costs of access to large language models, there will likely be a proliferation of chatbots for businesses big and small.
“This is just the beginning,” De Freitas of Character.AI said, noting that chatbots are going to get a lot more intelligent. “It will become a greater and greater part of people’s lives.”
Popular Chatbot Examples
ChatGPT
ChatGPT is one of the most-used chatbots, and is largely responsible for the influx of AI-based chat tools used today. Currently running on GPT-5, it can process text, images, audio and video to respond to user queries. ChatGPT uses deep learning and natural language processing to generate human-like text.
Gemini
Developed by Google with research from its DeepMind division, Gemini is a multimodal chatbot that generates human-like responses. It can be used to generate text, images and video. Gemini is currently powered by its 2.0 family models, which include Pro, Flash and Flash-lite, which offer varying processing and speed capabilities.
Perplexity
Perplexity is a search-optimized chatbot. Operating more like a search engine, the platform answers queries by analyzing content and summarizing it for the user. This conversational approach to internet searches is being expanded, as the company launched a standalone web browser that aims to compete directly with Google.
Claude
Claude is a family of large language models (Haiku, Sonnet and Opus) with optimizations for different tasks and speeds. Anthropic, the company behind the LLMs, is known for its use of constitutional AI, a training approach that applies a set of ethical principles to shape and guide a model’s responses. The Claude models can handle text and visual inputs, as well as large context windows, which also makes it it useful in code generation and review.
Grok
Grok is a chatbot developed by Elon Musk’s AI company xAI. It supports multimodal inputs, allowing users to type, speak or upload media as promptsThe company brands Grok as a “truth-seeking” companion and is known for being less politically correct than other chatbots.
Copilot
Copilot is a chatbot designed for integration across Microsoft’s product lineup and operating system. Built on a proprietary Prometheus model, Copilot can generate human-like text, images and code. Microsoft also provides specialized tools like Copilot Studio for building custom bots.
Le Chat
Le Chat is a conversational AI chatbot created by French AI company Mistral. The platform is a competitor to other chatbots, like ChatGPT, but includes unique features like Flash Answers, which provides rapid query responses. Its other features include real-time web search, multimodal understanding, image generation and an integrated code sandbox.
Watson Assistant
IBM features Watson Assistant on its website as an easy way to address customers’ basic questions. Combining natural language processing, deep learning and machine learning, Watson can aid customers with interactive demos, reaching IBM team members and explaining what Watson is.
Alexa
Alexa is Amazon’s voice-enabled chatbot that listens for voice commands on various devices and applications. It uses natural language processing to translate voice commands into text and conduct an analysis to understand the user’s intent. It then culls through its training data and APIs to come up with a response. Alexa has various automation capabilities and works well as a personal assistant for tasks like creating alarms or managing smart home devices.
History of Chatbots
For decades, chatbots were exclusively text-based, and were programmed to reply to only a limited set of statements and questions with answers that had been pre-written by human developers.
One of the earliest known examples of this is ELIZA, created by MIT professor Joseph Weizenbaum in the 1960s. With its simple design of predetermined statements, paired with keyword and pattern matching, ELIZA was able to mimic the conversational patterns of psychotherapists, and even trick some users into thinking it was just as intelligent as a human.
Over time, chatbots began integrating more sophisticated forms of artificial intelligence like natural language processing and natural language generation, allowing users to experience them in a more conversational way, as opposed to just simple Q&A.
Understanding how chatbots evolved from simple scripted agents to context-aware conversational AI helps frame their growing impact across industries and daily life. Here’s a look back at six foundational milestones that shaped the trajectory of chatbot technology.
Release of GPT-5 (August 2025)
OpenAI released GPT-5, its most advanced model to date, offering integrated fast-mode and reasoning-mode responses, as well asimproved performance in coding, math and domain-specific reasoning. The release included main, mini, and nano versions, along with four optional ChatGPT “personas” for tailored conversational style. Microsoft integrated GPT-5 into Copilot and Azure shortly after launch, underscoring its enterprise focus.
Gemini 2.5 Series Release (June 2025)
Google DeepMind expanded its Gemini suite with Gemini 2.5 Pro, Gemini 2.5 Flash, and Flash-Lite, combining improved reasoning, multimodal capabilities and faster response times. Pro was optimized for complex problem-solving and coding, while Flash targeted speed-critical use cases at lower cost. These updates were built on Gemini’s mixture-of-experts (MoE) architecture and marked the most advanced iteration of Google’s chatbot-capable models to date.
GPT-4o (Omni) Multimodal Launch (May 2024)
OpenAI introduced GPT-4 Omni (GPT-4o), a multimodal model capable of processing and generating text, images and audio. It offered faster responses, improved multilingual support and real-time conversational ability, setting new standards for AI assistants. GPT-4o Mini followed in July 2024, replacing GPT-3.5 Turbo in ChatGPT.
Google Gemini 1.0 Launch (December 2023)
Google DeepMind launched Gemini 1.0, the successor to Bard and PaLM 2, with Ultra, Pro, and Nano variants. Designed for use cases ranging from mobile integration to high-end reasoning tasks, Gemini 1.0 marked Google’s strategic consolidation of its AI assistant technologies under a single brand.
ChatGPT Public Release (November 2022)
OpenAI launched ChatGPT in November of 2022. Within five days, it reached more than 1 million users, quickly becoming one of the most widely recognized AI applications. ChatGPT ushered in a wave of large language model–based chatbots, which leverage transformer architectures and deep learning to generate human-like conversation and complete complex queries.
Microsoft XiaoIce Launch (September 2014)
Microsoft introduced XiaoIce, an empathetic social chatbot optimized for long-term user engagement. It employed emotional intelligence (EQ) in addition to traditional conversational capabilities (IQ), and by design achieved an average conversation-turns-per-session (CPS) of 23, significantly higher than previous chatbots. This marked a shift toward social and emotionally aware AI companions.
Chatbots in Messaging Platforms (April 2016)
Platforms like Facebook Messenger and WhatsApp opened to third-party bots. Facebook saw tens of thousands of bots emerge, enabling tasks like booking flights, customer service and conversational engagement directly within messaging apps.
Launch of Jabberwacky Chatbot (1997)
Developed by Rollo Carpenter, Jabberwacky was one of the first chatbots that learned from real-time user interactions rather than relying solely on scripted responses. It won the Loebner Prize in both 2005 and 2006, exemplifying a move toward more adaptive, dynamic conversational agents.
Launch of PARRY Chatbot (1972)
Designed by psychiatrist Kenneth Colby, PARRY simulated a person with paranoid schizophrenia and represented a step up from ELIZA with its conceptual models and judgment-based conversational strategies. It successfully passed variations of the Turing Test in psychiatric evaluations and was described as “ELIZA with attitude.”
Frequently Asked Questions
What is a chatbot?
A chatbot is an automated computer program that simulates human conversations. Using various systems like artificial intelligence and natural language processing, it can process human input and respond in a coherent manner.
What is a chatbot used for?
Chatbots are used for a variety of tasks. Because they can converse through text or voice they are used in areas like customer service, research and for personal assistance.
Are chatbots safe?
Chatbots are generally safe for use, but there are two considerations to keep in mind when using one.
- Personal information sharing - When using a chatbot, avoid sharing personal data like your name, age, address and payment information. Like anything relying on cloud services, chatbots can be susceptible to data breaches. It is best to keep that information in as few places as possible.
- Hallucinations - It is well recorded that generative AI and chatbots are prone to hallucination or presenting false information from time to time. This can result from various factors like limited data or not fully understanding context, but result in presenting false information in a truthful manner.
Is Alexa an AI chatbot?
Yes, Alexa is a type of voice-based AI chatbot. Available on various devices and applications, it understands spoken language and can be used for task automation and as a personal assistant.
