A Transformer-based deep learning model trained on massive text corpora — capable of understanding, generating, translating, summarizing, and reasoning about human language at unprecedented scale.
In Depth
A Large Language Model is a deep neural network trained on hundreds of billions (or trillions) of tokens of text — books, websites, code, scientific papers, and social media — with the objective of predicting what token comes next in a sequence. This seemingly simple task, applied at sufficient scale with the Transformer architecture, produces models that exhibit emergent capabilities far beyond next-word prediction: reasoning, summarization, coding, translation, and in-context learning.
LLMs are characterized by scale: billions to trillions of parameters, trained on terabytes of data using thousands of specialized processors over weeks or months. After pre-training, they are typically fine-tuned on task-specific data and aligned with human preferences through RLHF (Reinforcement Learning from Human Feedback) — the process that transforms a raw language model into a helpful, safe assistant. The resulting systems — GPT-4, Claude, Gemini, Llama — are the basis for most current AI products.
Despite their impressive capabilities, LLMs have important limitations. They can hallucinate — generating confident, fluent, but factually incorrect statements. They encode biases present in their training data. They struggle with tasks requiring precise numerical reasoning or strict logical deduction. They have knowledge cutoffs and cannot access real-time information without tools. Understanding these limitations is as important as appreciating the capabilities.
LLMs achieve remarkable language capabilities not through explicit rules but through massive-scale statistical learning from text — making them powerful generalists adaptable to nearly any language-based task.
Real-World Applications
Frequently Asked Questions
How are Large Language Models trained?
LLM training has three stages: (1) Pre-training — the model learns to predict the next token from trillions of words of text, developing broad language understanding. (2) Fine-tuning — the model is trained on higher-quality, task-specific data to follow instructions and engage in dialogue. (3) Alignment (RLHF) — human feedback shapes the model to be helpful, accurate, and safe. The process takes weeks on thousands of GPUs.
Why do LLMs hallucinate?
LLMs generate text by predicting statistically likely next tokens — they don't have a factual database to verify claims against. When a query falls outside their training data or requires precise factual recall, they generate plausible-sounding but incorrect text. They're optimized for fluency, not truth. This is why retrieval-augmented generation (RAG) and tool use are being integrated to ground LLM outputs in verified information.
What is the difference between GPT, Claude, and Gemini?
GPT (OpenAI), Claude (Anthropic), and Gemini (Google) are all Large Language Models based on the Transformer architecture, but they differ in training data, alignment approaches, and design priorities. Claude emphasizes safety and helpfulness through Constitutional AI. GPT-4 focuses on broad capability. Gemini integrates multimodal understanding natively. All are competitive on benchmarks with different strengths.