What is a Large Language Model (LLM)?
A large language model (LLM) is an AI system trained on billions of text examples that can understand and generate human language. It predicts the most likely next word or sentence based on patterns learned during training. Examples include GPT-4, Claude, and Gemini. Businesses use LLMs for writing assistance, customer service automation, data summarization, and building AI-powered applications.
A large language model is an AI system trained on vast amounts of text that can generate, summarize, translate, and answer questions in natural language.
How an LLM is trained
Training an LLM involves feeding it enormous amounts of text, including websites, books, code, and academic papers, and having it learn statistical patterns in language. The model adjusts billions of internal parameters until it can reliably predict what comes next in a piece of text.
This process requires significant computing power and takes weeks or months. The result is a model that has internalized a broad understanding of language, facts, and reasoning patterns from its training data. One important limitation: the model’s knowledge is frozen at a cutoff date. It does not know about events after training ended unless you provide that information in the prompt.
Tokens and context windows
LLMs process text as tokens, not words. A token is roughly 0.75 words in English. A model with a context window of 128,000 tokens can handle about 100,000 words in a single interaction — enough for a full book.
The context window determines how much information the model can work with at once. If you provide a long document, a conversation history, and a question, all of that counts toward the limit. Larger context windows allow for more complex tasks, such as analyzing entire contracts or codebases.
What businesses use LLMs for
LLMs are practical tools for a wide range of business tasks. Common use cases include drafting and editing text, summarizing long documents, translating content, answering questions based on provided source material, generating code, and classifying or extracting information from unstructured data.
The key to effective use is a clear prompt. An LLM produces better output when you specify the role it should play, the format you expect, and any constraints. See writing effective prompts for practical guidance.
Limitations to keep in mind
LLMs can generate incorrect information that sounds confident — a phenomenon known as hallucination. They also reflect biases present in their training data. For business-critical outputs, always treat LLM-generated content as a draft that requires review.
For applications that require up-to-date or proprietary information, retrieval-augmented generation (RAG) is a practical approach: you provide the model with relevant documents at query time, reducing the risk of outdated or fabricated answers.
Choosing between models
The major LLM providers are OpenAI (GPT series), Anthropic (Claude), and Google (Gemini). Each has different strengths, pricing, context window sizes, and data-handling policies. For applications that process sensitive business data, review the provider’s data retention and privacy terms before integrating.
AI agents take LLMs a step further by giving them tools to act — browsing the web, running code, or calling APIs — rather than just generating text.
Our tip: Start with a well-defined, narrow task rather than trying to replace an entire workflow with an LLM at once. Summarizing a weekly report or drafting a first version of a standard document are good starting points that let you evaluate quality and build confidence before expanding use.
Frequently asked questions
What is the difference between an LLM and a chatbot?
An LLM is the underlying AI model that understands and generates text. A chatbot is an application built on top of an LLM that provides a conversational interface. ChatGPT, for example, is a chatbot powered by OpenAI's GPT models. You can build your own chatbot using an LLM via an API, or use ready-made solutions. The LLM handles the language understanding; the chatbot handles the interface, context management, and any business logic on top.
What are hallucinations in LLMs and how do you deal with them?
Hallucinations are instances where an LLM generates plausible-sounding but incorrect information. This happens because the model predicts likely text rather than verifying facts. To reduce hallucinations: keep prompts specific and focused, ask the model to say when it is uncertain, provide relevant source documents in the prompt (retrieval-augmented generation), and always verify critical outputs against authoritative sources. For business-critical applications, treat LLM output as a draft that requires human review.
What does context window mean for LLMs?
The context window is the maximum amount of text an LLM can process in one interaction, measured in tokens (roughly 0.75 words per token). A model with a 128,000-token context window can handle about 100,000 words at once, enough for a full book. The context window matters when you want to feed the model large documents or long conversation histories. If your input exceeds the context window, the model cannot process it in one go and you need to split it or summarize earlier parts.