Large Language Model (LLM)

Large Language Models (LLMs) are deep learning models trained on vast amounts of text data to understand and generate human language. Models like GPT-4, Claude, Gemini, and Llama have billions of parameters and can perform a wide range of language tasks.

LLMs work by predicting the next token (word or sub-word) in a sequence, using the transformer architecture. During training, they learn patterns in grammar, facts, reasoning, and even code from their training corpus.

Key capabilities include text generation, summarization, translation, question answering, code generation, and reasoning. LLMs can be used through APIs (like OpenAI or Anthropic) or deployed locally using open-source models.

Important considerations when working with LLMs include token limits (context window), cost per token, latency, hallucination risks, and the need for prompt engineering or fine-tuning to achieve optimal results for specific use cases.