Large Language Model
A type of AI model trained on massive amounts of text data that can understand and generate human-like text. LLMs use transformer architecture and typically have billions of parameters, enabling them to perform a wide range of language tasks.
Why It Matters
LLMs like GPT-4, Claude, and Gemini are driving the current AI revolution. They power chatbots, coding assistants, content generation, and enterprise search.
Example
Claude answering complex questions, summarizing documents, writing code, or helping brainstorm ideas — all from a single model.
Think of it like...
Like a person who has read every book in the world's largest library and can now have an informed conversation about virtually any topic.
Related Terms
Transformer
A neural network architecture introduced in 2017 that uses self-attention mechanisms to process sequential data in parallel rather than sequentially. Transformers are the foundation of modern LLMs like GPT, Claude, and Gemini.
GPT
Generative Pre-trained Transformer — a family of large language models developed by OpenAI. GPT models are trained to predict the next token in a sequence and can generate coherent, contextually relevant text across many tasks.
Token
The basic unit of text that language models process. A token can be a word, part of a word, or a punctuation mark. Text is broken into tokens before being fed into an LLM, and the model generates output one token at a time.
Context Window
The maximum amount of text (measured in tokens) that a language model can process in a single interaction. It includes both the input prompt and the generated output. Larger context windows allow models to handle longer documents.
Fine-Tuning
The process of taking a pre-trained model and further training it on a smaller, domain-specific dataset to specialize its behavior for a particular task or domain. Fine-tuning adjusts the model's weights to improve performance on the target task.
Prompt Engineering
The practice of designing and optimizing input prompts to get the best possible output from AI models. It involves crafting instructions, providing examples, and structuring queries to guide the model toward desired responses.