Artificial intelligence (AI): The branch of computer science that deals with creating machines that can perform tasks that normally require human intelligence, such as understanding natural language or recognizing images.

Auto-completion: The feature of a large language model like GPT that suggests the most likely next word or phrase based on the input given.

Chatbot: A computer program that simulates conversation with human users, often powered by large language models like GPT.

ChatGPT: ChatGPT is a chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and has been fine-tuned using both supervised and reinforcement learning techniques.

Content generation: The ability of a large language model like GPT to generate text or other content based on a given prompt, such as writing a blog post or product description.

Deep learning: A type of machine learning that uses neural networks to process and learn from large amounts of data, often used in large language models like GPT.

Fine-tuning: The process of customizing a pre-trained model, such as GPT-3, to a specific task or domain by training it on additional data.

Generative model: A type of machine learning model that can generate new data, such as text or images, based on existing examples.

Generative Pre-trained Transformer 3 (GPT-3): An autoregressive language model released in 2020 that uses deep learning to produce human-like text. Given an initial text as prompt, it will produce text that continues the prompt.

Language model: A type of model that is trained to predict the probability of the next word in a sequence of text, often used in large language models like GPT.

Language translation: The ability of a large language model like GPT to translate text from one language to another, such as translating English to Spanish.

Large language models (LLMs): Neural network models trained on large amounts of text data to perform tasks such as text generation, language translation, or question answering.

Natural language: The language that humans use to communicate with each other, such as English or Spanish.

Natural language processing (NLP): The branch of AI that deals with the interaction between computers and human language, such as understanding or generating human-like text using LLMs.

Neural network: A type of machine learning model that is designed to simulate the structure and function of the human brain, often used in large language models like GPT-3.

OpenAI: An artificial intelligence research laboratory consisting of the for-profit OpenAI LP and the non-profit OpenAI Inc.

Personalization: The feature of a large language model like GPT that can customize its output based on the user's preferences or past interactions.

Prompt: The input given to a large language model like GPT to generate a specific output, such as a question or a statement.

Prompt engineering: The process of crafting an input or prompt for a large language model like GPT to generate a specific output, such as writing a product review or creating a chatbot response.

Question answering: The ability of a large language model like GPT to answer questions based on a given prompt, such as answering factual or opinion-based questions.

Reinforcement learning: A type of machine learning where the model learns through trial-and-error by receiving feedback in the form of rewards or penalties for its actions.

Sentiment analysis: The process of using a large language model like GPT to determine the emotional tone of a piece of text, such as identifying whether a product review is positive or negative.

Supervised learning: A type of machine learning where the model is trained on labeled data, where the correct outputs are already known.

Text generation: The process of using a large language model like GPT to generate new text based on a given prompt or input.

Text summarization: The ability of a large language model like GPT to summarize a long piece of text, such as an article or document, into a shorter summary.

Transformer: A type of neural network architecture that is commonly used in large language models like GPT, and is designed to handle sequential data and capture long-range dependencies.

Voice assistants: Digital assistants that use large language models like GPT to perform tasks or answer questions for users using spoken language, such as Apple's Siri or Amazon's Alexa.