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Large language model (LLM)
A large language model is an AI system trained on very large amounts of text to predict the next piece of text. That simple skill lets it draft, summarize, translate, classify and work through problems. Claude, GPT, Gemini and Grok are examples of LLM families. ChatGPT and similar apps are products built on top of them.
What it is
An LLM is a neural network trained on large collections of text, and often images and audio too. It works in tokens, small chunks of text. It generates output one token at a time, based on everything in its context window. IBM groups LLMs under generative AI: models that create new content in response to a prompt.
How it works
Two phases matter for business users:
| Phase | What happens | Who does it | How often |
|---|---|---|---|
| Training | The model learns patterns from a large corpus, up to a knowledge cutoff. Vendors then tune it to follow instructions and refuse harmful requests. | The vendor | Once per model version |
| Use (inference) | You send a prompt and the model predicts a response. Apps add memory, search and tools around it. | You, every request | Every time |
Vendors publish model lists with context sizes, cutoffs and prices. Anthropic and OpenAI both document theirs.
Benefits
- General-purpose: one model handles writing, analysis, coding and more.
- Works in plain language, so non-technical staff can use it.
- Can be connected to your data and tools to do real work.
Limits and risks
- Can state false things confidently (see hallucination).
- Knows nothing after its cutoff unless given search or documents.
- Output varies between runs and between models.
Agent compatibility
Not tool-specific. See Agent-ready for connector support by tool.
Levels come from each tool's recorded, sourced compatibility. How the levels work.
Related entries
Sources
- What is generative AI? · IBMreference accessed Oct 17, 2026
- Models overview · Anthropicofficial accessed Oct 17, 2026
- Models · OpenAIofficial accessed Oct 17, 2026
Practitioner views are restated in our words and labeled as views, not measured results. Product facts come from official sources dated above.
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