What is a Large Language Model? - Explanation & Meaning

Definition

A large language model (LLM) is a type of AI model trained on vast amounts of text data to understand, generate, and reason with human language. Well-known examples include GPT-5 by OpenAI, Claude 4 by Anthropic, and Gemini 2 by Google.

Technical explanation

LLMs are built on the transformer architecture, introduced in the "Attention Is All You Need" paper (2017). At the core is the self-attention mechanism, which enables the model to analyze relationships between all words in a text simultaneously. Modern LLMs contain hundreds of billions of parameters — adjustable weights optimized during training. The training process consists of two phases: pre-training on massive text corpora via next-token prediction, followed by alignment through Reinforcement Learning from Human Feedback (RLHF) or Direct Preference Optimization (DPO). In 2026, the LLM landscape has diversified: alongside closed models from OpenAI and Anthropic, open-source models like Llama 4 and Mistral Large have become competitive. Context windows have grown to millions of tokens, enabling processing of complete documents in a single pass. Multimodal LLMs now process text, images, audio, and video within a unified architecture.

How MG Software applies this

At MG Software, we use LLMs as the core of the AI solutions we build. We integrate models via APIs from OpenAI, Anthropic, and Google, combining them with RAG systems to make business-specific knowledge accessible. For clients with strict data privacy requirements, we deploy open-source models on their own infrastructure.

Practical examples

• A customer service department deploying an LLM-powered chatbot to automatically answer 80% of incoming queries with context-aware, personalized responses based on customer history.
• A research institute using an LLM to summarize scientific papers, extract key findings, and identify connections between publications, saving researchers hours weekly.
• A software development team using an LLM as a code assistant that writes functions, identifies bugs, and generates documentation directly in the IDE.

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