AI Glossaryfundamentals

Large Language Model (LLM)

A neural network trained on massive text datasets that can generate, understand, and reason about human language.

How It Works

LLMs like GPT-5, Claude, and Gemini are the foundation of modern AI applications. They work by predicting the next token (word or subword) in a sequence, but through scale (billions of parameters) and training on internet-scale data, they develop emergent capabilities like reasoning, coding, and following complex instructions. In production, LLMs are accessed via APIs from providers like OpenAI, Anthropic, and Google.

Common Use Cases

  • 1Chatbots and virtual assistants
  • 2Content generation
  • 3Code completion
  • 4Document analysis
  • 5Translation

Related Terms

Transformer

The neural network architecture behind all modern LLMs, using self-attention mechanisms to process sequences in parallel.

Tokenization

The process of breaking text into smaller units (tokens) that an AI model can process, typically subwords or word pieces.

Fine-Tuning

The process of further training a pre-trained AI model on your specific data to improve performance on domain-specific tasks.

Prompt Engineering

The practice of crafting effective instructions for AI models to produce desired outputs consistently.

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