What is an LLM?
Large Language Models represent one of the most powerful breakthroughs in modern AI. Unlike traditional models that specialize in narrow tasks, LLMs are foundation models - trained on massive datasets spanning books, articles, code, and web text.
These models learn linguistic patterns, world knowledge, and contextual reasoning by predicting the next word in a sequence. Once trained, they can generalize across domains - drafting essays, writing code, summarizing documents, or conversing naturally.
LLMs are the engine behind Generative AI, AI copilots, and conversational agents that make human-machine interaction feel fluid and intelligent. LLMs use deep learning - particularly the transformer architecture - to perform tasks such as text generation, summarization, translation, code completion, and question answering.
Famous examples include GPT-4, Claude, Gemini, and LLaMA.
How LLMs Work
- Pretraining:
The model learns language patterns from large corpora through unsupervised learning (predicting missing or next tokens). - Architecture:
LLMs use transformer neural networks - layers of attention mechanisms that let the model focus on context and relationships between words. - Fine-Tuning:
After pretraining, models are refined on domain-specific or instruction-based data to improve task performance. - Inference:
When prompted, the model generates responses by sampling likely tokens based on context. - Reinforcement Learning with Human Feedback (RLHF):
Human reviewers rate outputs to guide model alignment, improving quality and safety.
