TL;DR

Software that holds a conversation with people automatically, from scripted keyword bots to AI assistants that understand free-form language.

Key Points

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Two broad families exist: rule-based bots that follow if-then scripts and keyword matching, and AI bots that use [[natural-language-processing]] to read free-form text [1][2].

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Rule-based bots resolve a limited share of requests on their own, while AI agents wired into business systems report far higher self-resolution rates [3].

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AI chatbots rely on [[intent-recognition]] to map a message to what the user actually wants, increasingly powered by a [[large-language-model]] [1][2].

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A good bot offers a clear [[bot-to-human-handoff]] so a complex or sensitive [[conversation]] reaches a [[support-operator]] with context attached [3].

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Chatbots differ from [[live-chat]]: chat connects a visitor to a real person, whereas a chatbot answers automatically and runs 24/7 [2][3].

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The technology dates back to ELIZA in 1966 and shifted from scripted matching to today's generative, LLM-based assistants [1].

Rule-based bots versus AI chatbots

Chatbots fall into two broad categories. Rule-based bots follow predetermined decision trees: they match a visitor's input against set keywords or button choices and return fixed replies, which makes them predictable but brittle when wording strays from the script ^[1]^[2]. AI chatbots instead apply Natural Language Processing (NLP) and machine learning to interpret meaning, using Intent Recognition to figure out what the person wants regardless of exact phrasing ^[2]. Modern AI assistants are increasingly built on a Large Language Model (LLM), generating unscripted answers rather than reciting canned text ^[1]. The practical trade-off is coverage versus control: scripted bots resolve a narrower band of routine requests, while AI agents connected to order and CRM systems report substantially higher self-resolution rates ^[3].

Chatbots in customer support

In support, chatbots aim to deflect repetitive questions so human staff can focus on harder problems. They answer FAQs, look up orders, and guide Self-Service Support, often drawing on a Knowledge Base to ground their replies ^[3]. The defining limit is judgment: when a request is ambiguous, emotional, or high-stakes, a well-designed bot performs a Bot-to-Human Handoff, passing the Conversation and its history to a Support Operator rather than guessing ^[3]. This is why a chatbot is not a replacement for Live Chat but a front line to it. Generative bots can also hallucinate, presenting wrong information confidently, so teams pair them with curated content and a reliable escalation path to a person ^[1]^[3].

Where Eloqra fits

Eloqra deliberately takes a human-first path rather than shipping a chatbot. Its JavaScript Widget forwards each visitor message to the owner on Telegram, where a real operator reads context like the page URL and location and replies directly, so answers come from a person, not an automated script. Founders comparing options should see the two approaches as complementary: a chatbot is strong at instant, round-the-clock deflection of routine questions, while a tool like Eloqra prioritizes genuine human replies for the conversations where empathy and accountability matter. Many businesses end up running both, letting a bot field FAQs and routing anything nuanced to a human through Live Chat.

Sources & References

Chatbot — Wikipedia ↗

What Is a Chatbot? Definition, Types and How They Work — Tidio ↗

Customer Service Chatbot: How It Works and Why You Need One — Tidio ↗

Last updated: June 25, 2026

Related Terms

Conversational AI

Conversational AI is a class of artificial intelligence that uses natural language processing and machine learning to understand, interpret, and respond to human language in a free-form, human-like dialogue. In customer messaging it powers chatbots and virtual agents that hold real conversations instead of following a fixed script.

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a subfield of artificial intelligence that lets computers interpret, manipulate, and generate human language in text or speech. In customer messaging it is the technology that turns a free-form question into something software can understand and act on.

Intent Recognition

Intent recognition (also called intent detection or intent classification) is the process by which a system identifies the goal behind a user's message, such as asking a question, requesting a refund, or booking an appointment. It is a core function of natural language understanding that maps free-form text to a predefined set of intents so software can respond appropriately.

Bot-to-Human Handoff

Bot-to-human handoff is the moment a chatbot or AI agent transfers an active conversation to a live human operator, passing along the context it has gathered. It exists so automation can resolve routine questions while a person steps in for issues the bot cannot, or should not, handle alone.

Large Language Model (LLM)

A large language model (LLM) is a transformer-based neural network trained on vast amounts of text to predict the next token, which lets it understand, summarize, and generate human-like language. [1] In support tools, LLMs power chatbots and AI agents that draft replies and answer questions in natural language.

Live Chat

Live chat is a real-time, text-based support channel embedded in a website or app that lets visitors message a business and get answers within the same browsing session, without phoning or emailing. It pairs a customer-facing chat window with an agent or bot interface on the other side.

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