TL;DR

An LLM is an AI model trained on huge text datasets that generates human-like language, and it is the engine behind modern support chatbots and AI agents.

Key Points

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LLMs use a transformer architecture with billions of parameters, learning patterns from text corpora measured in petabytes to predict the most likely next word. [1][2]

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They are the engine behind a [[chatbot]] and [[conversational-ai]], enabling natural-language replies instead of rigid keyword scripts. [3]

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Paired with a [[knowledge-base]] via retrieval, an LLM can draft grounded answers and support [[ticket-deflection]] before a human is involved. [3]

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A known weakness is hallucination: LLMs can produce fluent text that is confidently wrong, so high-stakes replies still need a [[bot-to-human-handoff]]. [1][2]

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LLMs improve [[natural-language-understanding]] and [[intent-recognition]], helping route or summarize a [[conversation]] more accurately than older bots. [3]

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The same models also power an [[ai-coding-agent]] that can read instructions and install tools like a [[live-chat-widget]] for you.

How an LLM works

An LLM is a deep-learning model built on the transformer architecture, which uses a self-attention mechanism to weigh how tokens (words or word fragments) relate to each other across a passage. ^[1] During pretraining it ingests massive unlabeled text corpora and learns to predict the next token, accumulating billions of parameters that encode statistical patterns of language. ^[1]^[2] Models are then often fine-tuned, including with reinforcement learning from human feedback, to follow instructions and stay on topic. ^[2] The result is a system that can summarize, translate, classify, and generate fluent text, which is why it underpins modern Conversational AI and Natural Language Processing (NLP) features rather than the brittle keyword matching used by earlier bots.

LLMs in live chat and support

In customer messaging, LLMs let a Chatbot understand a question phrased many different ways and respond in plain language. Vendors such as Intercom pair an LLM with retrieval over a Knowledge Base so the AI Agent answers using only relevant help content, which improves accuracy and drives Ticket Deflection; it escalates to a person when confidence is low. ^[3] LLMs also assist human teams behind the scenes by drafting replies, suggesting a Canned Response, summarizing a Chat Transcript, or tagging conversations. Because they grasp intent without exact phrasing, they raise First Contact Resolution (FCR) for routine questions while freeing a Support Operator to focus on complex cases. ^[3]

Limits and where Eloqra fits

LLMs are powerful but imperfect: they can hallucinate, reflect bias in training data, and are costly to run, so outputs need guardrails and a clean path to a human. ^[1]^[2] For that reason, every automated chat surface should keep a reliable Bot-to-Human Handoff. Eloqra itself is a lightweight Live Chat tool that forwards visitor messages to operators on Telegram rather than bundling an LLM autoresponder; it ships no built-in AI agent today. Where LLMs touch Eloqra is installation: because setup is a single Embed Code snippet, an AI Coding Agent like Claude Code or Cursor can add the JavaScript Widget to your site from a short instruction.

Sources & References

What are Large Language Models (LLMs)? - TechTarget ↗

Large language model - Wikipedia ↗

Fin AI Agent explained - Intercom Help ↗

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.

Chatbot

A chatbot is a software application that simulates a human-like conversation through text or speech, used to answer questions and complete tasks automatically. In customer support it ranges from simple rule-based scripts to AI-driven assistants that interpret natural language and resolve requests without a human agent.

AI Agent

An AI agent is an autonomous software system that uses a large language model to understand a customer request, reason about it, take actions through connected tools, and resolve the issue end to end with minimal human input. In support, it goes beyond answering questions to actually completing tasks and handing off to a person when needed.

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.

Knowledge Base

A knowledge base is an organized, searchable library of articles, FAQs, how-to guides, and troubleshooting steps that lets customers find answers about a product or service on their own. In customer support it is the backbone of self-service, available around the clock without an agent. [1][2]

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.

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Large Language Model (LLM)