LLM (Large Language Model) (النموذج اللغوي الكبير)
A large language model (LLM) is an AI system trained on massive amounts of text to understand and generate human language. It predicts the most likely next words, enabling it to answer questions, write, translate, summarize, and power chatbots and AI agents.
In Iraqi dialect
عقل اصطناعي يفهم اللغة ويكتب ويجاوب
In detail
LLMs are neural networks, usually transformers, with billions of parameters learned from books, websites, and code. During training they learn statistical patterns of language; at inference they generate text one token at a time. Their breadth makes them general-purpose engines for reasoning about language, but they can hallucinate and their knowledge has a cutoff. In production, businesses combine an LLM with retrieval (RAG) over their own documents, guardrails, and fine-tuning to make answers accurate and on-brand. LLMs are the reasoning core inside modern chatbots, voice agents, and agentic AI systems.
Practical example
A support team fine-tunes an LLM on its manuals so answers match company policy instead of generic web content.
Frequently asked questions
Can an LLM make things up?
Yes — LLMs can hallucinate. Pairing them with retrieval over verified data and guardrails reduces this risk.
What is fine-tuning an LLM?
It is additional training on a specific dataset so the model adapts to a domain, tone, or task.
Related terms
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