An AI system trained on vast amounts of text to predict and generate human-like language. Best understood as a concept-transformation engine rather than a knowledge database.
Definition: An AI system trained on vast amounts of text to predict and generate human-like language. Best understood as a concept-transformation engine rather than a knowledge database.
A Large Language Model (LLM) is an AI system that has been trained on enormous amounts of text data to learn statistical relationships between words and concepts. Models like GPT, Claude, and Gemini are examples of LLMs.
At their core, LLMs are advanced prediction machines. When you provide a prompt, the model does not "think" or "know" the answer—it calculates the most probable next token (word or word-part) based on patterns learned during training.
The "T" in GPT stands for Transformer, which describes the model's core function: transforming concepts from one form to another.
Understanding this architecture is key to using LLMs effectively:
Treat an LLM as a powerful autocomplete and concept connector, not as a perfectly factual source. The best results come from giving it structured tasks to perform on information you provide, rather than asking it to generate knowledge from scratch.
When an AI model generates plausible-sounding but factually incorrect or fabricated information. A natural consequence of how LLMs predict probable text rather than verify truth.
A technique that enhances LLM responses by first retrieving relevant information from a specific knowledge base, then using that information to ground the model's output.
This term is referenced in the following articles:
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