What Is an LLM?
A large language model (LLM) is a type of neural network — typically a transformer-based deep learning model — trained on large text corpora (often hundreds of billions of words) that can generate, understand, and reason about human language. Examples in 2026: GPT-5, Claude Opus 4, Gemini 2.5 Pro.
Frequently Asked Questions
What is an LLM in simple terms?
An LLM is a computer program that learned to predict the next word in a sentence by reading enormous amounts of text. Once trained, it can write essays, answer questions, summarize documents, generate code, and have conversations. ChatGPT, Claude, and Gemini are all LLM-based products.
How do LLMs work?
LLMs use a neural network architecture called a "transformer" that processes text as sequences of tokens (chunks roughly equal to ~4 characters of English). During training, the model learns patterns by predicting masked or next tokens across billions of examples. After training, the model generates outputs by sequentially predicting the most likely next token given context.
What is the difference between GPT, Claude, and Gemini?
They are all LLMs but trained by different organizations with different design choices. GPT (OpenAI) tends to be strong at iteration and tool use. Claude (Anthropic) is strong at long-context reasoning, prose, and tonal precision. Gemini (Google) is strong at multimodal handling and Google Workspace integration. See our guides at /best-chatgpt-prompts/, /best-claude-prompts/, and /best-gemini-prompts/.
What can LLMs not do?
LLMs cannot reliably do: novel mathematical proof, real-time information without web search, physical-world reasoning beyond training data, deeply specialized domain reasoning without fine-tuning, and reliable factual accuracy on specific facts (they can hallucinate). They also cannot maintain continuous memory across separate sessions without external infrastructure.
What is a token in an LLM?
A token is the basic unit an LLM processes — usually a subword fragment, roughly 4 characters of English text. The sentence "Promptolis is great" is approximately 5 tokens. Models price API usage per million tokens. See our /tools/token-counter/ for live counts across models.
What is fine-tuning?
Fine-tuning is the process of further training an LLM on specific data after the initial training, usually to specialize it for a domain (medical, legal, code) or task. Most users do not need to fine-tune in 2026; properly-engineered prompts on frontier models often perform comparably.
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