Large Language Model (LLM) is a machine-learning model trained on large corpora to predict and generate language or code sequences.
Simple definition
Large Language Model (LLM) is a machine-learning model trained on large corpora to predict and generate language or code sequences. It belongs to the IA / Data vocabulary and is useful when reading architecture diagrams, product documentation, logs, or administration procedures.
What is it used for?
Its main purpose is to generate, transform, summarize, or analyze text and serve as an engine for assistants and conversational applications. The practical value depends on the surrounding architecture, security model, and operational requirements.
How does it work?
An LLM tokenizes input, computes contextual representations with a transformer-style neural network, then generates successive tokens according to conditional probabilities.
Key points
- Scope: A machine-learning model trained on large corpora to predict and generate language or code sequences.
- Operational goal: Generate, transform, summarize, or analyze text and serve as an engine for assistants and conversational applications.
- Implementation: An LLM tokenizes input, computes contextual representations with a transformer-style neural network, then generates successive tokens according to conditional probabilities.
Points to watch
An LLM can hallucinate or reproduce biases; use validation, sources, tool boundaries, and human controls for sensitive uses.
In short
Large Language Model (LLM) = a machine-learning model trained on large corpora to predict and generate language or code sequences. Use it when you need to generate, transform, summarize, or analyze text and serve as an engine for assistants and conversational applications.