Large Language Model

A Large Language Model (LLM) is a transformer-based AI model trained on massive text corpora to generate, summarize, translate, and reason over language. Modern LLMs contain billions to trillions of parameters and are trained on hundreds of billions of tokens. Notable examples include OpenAI's GPT-4, Anthropic's Claude 3, Google's Gemini, Meta's open-weight Llama family, Mistral's models, and Chinese systems like Qwen and DeepSeek. LLMs power chatbots, code assistants (GitHub Copilot, Cursor), search engines (Perplexity), document summarization, customer support automation, and increasingly agentic workflows. They are typically delivered via APIs from cloud providers or self-hosted using open weights and runtimes like vLLM, llama.cpp, or Ollama. LLMs are reshaping every industry and sit at the center of nearly every AI governance conversation. Enterprise AI programs require strict AI compliance, AI risk management, and responsible AI controls around LLM deployment — covering prompt safety, data leakage, bias, hallucination, and cost.

Centralpoint Is the Governance Layer Every LLM Needs: Oxcyon built Centralpoint specifically for the LLM era. The platform is model-agnostic — call OpenAI's ChatGPT, Google Gemini, Meta Llama, or on-premise embedded models — meters every token, keeps prompts and skills strictly on-prem, and embeds multiple branded chatbots across your sites and portals with a single line of JavaScript.


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