A Large Language Model (LLM) is a type of AI model trained on massive text datasets to understand and generate human language. LLMs power tools like ChatGPT, GitHub Copilot, and many modern AI assistants.
An LLM (Large Language Model) is a deep learning model built on the Transformer architecture that predicts and generates text by learning statistical patterns from billions of words of training data. The 'large' refers both to the volume of training data and the number of model parameters, which can range from millions to hundreds of billions. Parameters are the internal numerical weights the model adjusts during training to capture language structure and meaning.
LLMs have dramatically lowered the barrier to building natural language applications such as chatbots, code assistants, document summarizers, and search engines. They generalize across tasks — a single model can translate, write code, answer questions, and classify sentiment — without task-specific retraining. This general-purpose capability makes them one of the most commercially impactful AI technologies in history.
LLMs are pre-trained using self-supervised learning: the model is given text and tasked with predicting the next token (a word or sub-word unit), adjusting its parameters via backpropagation to minimize prediction error. This is done at massive scale across diverse internet text, books, and code. After pre-training, models are often fine-tuned using Reinforcement Learning from Human Feedback (RLHF) to align outputs with human preferences and safety guidelines.
At inference time, the model receives a prompt as a sequence of tokens and generates a response token-by-token using a mechanism called autoregressive decoding. At each step, the model computes a probability distribution over its entire vocabulary and samples the next token, then appends it to the context and repeats. The Transformer's self-attention mechanism allows every token to 'attend' to every other token in the context window, giving the model rich contextual understanding.
Context window is the maximum number of tokens an LLM can process at once — exceeding it causes the model to lose earlier information. Temperature is a sampling parameter: low values make output more deterministic, high values make it more creative and varied. Prompt engineering — crafting precise inputs — is essential because LLMs are highly sensitive to how a question or instruction is phrased.
LLMs can 'hallucinate': generating confident-sounding but factually incorrect information, because they optimize for plausible text rather than verified truth. Always validate LLM outputs in high-stakes domains like medicine, law, or finance. Use Retrieval-Augmented Generation (RAG) to ground model responses in authoritative source documents, and set a low temperature for factual tasks to reduce randomness.
© RM Full Stack & AI Engineer · All guides · Roadmaps · Open the app