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AI Glossary

Large Language Model (LLM)

A deep learning model trained on vast text datasets that can understand and generate human-like text.

TL;DR

  • —A deep learning model trained on vast text datasets that can understand and generate human-like text.
  • —Understanding Large Language Model (LLM) is critical for effective AI for companies.
  • —Remova helps companies implement this technology safely.

In Depth

Large Language Models are neural networks with billions of parameters trained on internet-scale text data. They power modern AI assistants like ChatGPT (GPT-4), Claude, and Gemini. LLMs can perform tasks including text generation, summarization, translation, coding, and reasoning. Enterprise deployment of LLMs requires governance controls to manage costs, protect data, and ensure appropriate usage.

Related Terms

Foundation Model

A large AI model trained on broad data that can be adapted to many downstream tasks.

Token

The basic unit of text processing in LLMs — typically a word, subword, or character that models use for input and output.

Context Window

The maximum amount of text (measured in tokens) that an AI model can process in a single conversation.

Inference Cost

The computational cost of running a query through an AI model, typically measured per token.

Knowledge Hub

Glossary FAQs

Large Language Model (LLM) is a fundamental concept in the AI for companies landscape because it directly impacts how organizations manage a deep learning model trained on vast text datasets that can understand and generate human-like text.. Understanding this is crucial for maintaining AI security and compliance.
Remova's platform is built to natively manage and optimize Large Language Model (LLM) through our integrated governance layer, ensuring that your organization benefits from this technology while mitigating its inherent risks.
You can explore our full AI for companies glossary, which includes detailed definitions for related concepts like Foundation Model and Token.

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