What is a large language model?

A large language model is a deep learning system trained on vast collections of text so it can understand, interpret, and generate human language with a high degree of fluency.

How does a large language model work?

Large language models (LLMs) are advanced AI systems trained on enormous text corpora to perform tasks such as generating content, summarizing documents, translating languages, classifying information, and analyzing sentiment. Well-known examples include BERT, PaLM, GPT-2, GPT-3, GPT-3.5, and the multimodal GPT-4. These models differ in size, training data, and the types of problems they’re optimized to solve.

At the core of an LLM is the transformer architecture. The transformer is a neural network design built for parallel processing, which allows the model to handle long sequences of text efficiently. Its key component is the attention mechanism, which evaluates how strongly each word in a sentence relates to every other word. By learning these statistical relationships, the model builds a deep understanding of meaning, context, and intent.

During training, the model predicts the next token in a sequence over and over again. This seemingly simple task teaches it grammar, world knowledge, reasoning patterns, and linguistic structure. Once trained, the model can generate coherent paragraphs, follow instructions, answer questions, and adapt to a wide range of natural language tasks with minimal additional tuning.

Why are large language models important?

Large language models represent a foundational shift in how organizations can use AI. They enable conversational interfaces, automate repeated workflows, extract insights from unstructured information, and support employees with intelligent guidance. Because LLMs are pre-trained on massive datasets, companies can benefit from their capabilities without needing to train their own models from scratch.

LLMs help organizations deliver better user experiences, operate more efficiently, and reduce costs tied to manual tasks. However, to use them effectively, enterprises must understand their strengths and limitations, including latency, accuracy, grounding, and the need for oversight.

A growing trend is the use of enterprise AI assistants powered by LLMs. Tools like Moveworks’ enterprise assistant, Microsoft 365’s AI Copilot, GitHub Copilot, and Salesforce Einstein show how conversational interfaces can streamline work across IT, HR, finance, and customer support. As more companies adopt this pattern, having a clear strategy for deploying AI assistants becomes essential.

Why large language models matter for companies

Large language models provide businesses with powerful capabilities that improve productivity, customer satisfaction, and overall efficiency. They enable automated responses, personalized recommendations, intelligent search, and workflow acceleration across departments.

By handling repetitive or time-consuming tasks, LLMs free employees to focus on work that requires human judgment. They also help organizations react more quickly to market changes, uncover insights hidden in data, and enhance digital experiences for customers and employees alike.

In today’s competitive landscape, LLMs are not just an advantage — they are becoming a core enabler for innovation and operational excellence.