Glossary

Latency Definition The time delay between a request to an AI model and the receipt of its response. Detailed Explanation Latency is a fundamental concept in Infrastructure that refers to the time delay between a request to an ai model and the receipt of its response. Professionals in the field often use Latency in conjunction with other technologies to build robust solutions. Applications of LatencyReal-world applications include advanced natural language processing, computer vision systems, and automated decision-making frameworks. ...

Machine Learning Definition Subset of AI focusing on the development of algorithms that can learn from and make predictions on data. Detailed Explanation Machine Learning is a fundamental concept in Ai Core that refers to subset of ai focusing on the development of algorithms that can learn from and make predictions on data. Professionals in the field often use Machine Learning in conjunction with other technologies to build robust solutions. ...

Multi-Agent System Definition A system composed of multiple interacting intelligent agents solving problems that are difficult or impossible for an individual agent. Detailed Explanation Multi-Agent System is a fundamental concept in Agents that refers to a system composed of multiple interacting intelligent agents solving problems that are difficult or impossible for an individual agent. At its core, Multi-Agent System solves a specific problem in the AI landscape. Unlike traditional approaches, it leverages advanced algorithms to process data more efficiently. ...

Natural Language Processing Definition A branch of artificial intelligence that helps computers understand, interpret and manipulate human language. Last updated: February 2026

Neural Network Definition Computing systems inspired by the biological neural networks that constitute animal brains. Detailed Explanation Understanding Neural Network is crucial for mastering modern AI. It describes computing systems inspired by the biological neural networks that constitute animal brains. Professionals in the field often use Neural Network in conjunction with other technologies to build robust solutions. Applications of Neural NetworkReal-world applications include advanced natural language processing, computer vision systems, and automated decision-making frameworks. ...

Overfitting Definition When a model learns the training data too well, including noise, and fails to generalize to new data. Detailed Explanation Understanding Overfitting is crucial for mastering modern AI. It describes when a model learns the training data too well, including noise, and fails to generalize to new data. The significance of Overfitting cannot be overstated. As AI systems become more complex, mechanisms like this ensure scalability and accuracy. ...

Parameter Definition Internal variables of the model that are learned during training (weights and biases). Detailed Explanation Parameter is a fundamental concept in Architecture that refers to internal variables of the model that are learned during training (weights and biases). The significance of Parameter cannot be overstated. As AI systems become more complex, mechanisms like this ensure scalability and accuracy. Applications of ParameterReal-world applications include advanced natural language processing, computer vision systems, and automated decision-making frameworks. ...

Pre-training Definition The initial phase of training a model on a massive dataset to learn general patterns. Detailed Explanation Understanding Pre-training is crucial for mastering modern AI. It describes the initial phase of training a model on a massive dataset to learn general patterns. Professionals in the field often use Pre-training in conjunction with other technologies to build robust solutions. Why Pre-training MattersFor developers and data scientists, mastering Pre-training unlocks new capabilities in model design. It is particularly relevant for optimizing performance and reducing costs. ...

Prompt Engineering Definition The process of structuring text that can be interpreted and understood by a generative AI model. Detailed Explanation In the world of Skill, Prompt Engineering is defined as the process of structuring text that can be interpreted and understood by a generative ai model. At its core, Prompt Engineering solves a specific problem in the AI landscape. Unlike traditional approaches, it leverages advanced algorithms to process data more efficiently. ...

Reinforcement Learning Definition An area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Last updated: February 2026

Reinforcement Learning from Human Feedback Definition A technique used to align AI models with human values by using human feedback as a reward signal. Detailed Explanation In the world of Training, Reinforcement Learning from Human Feedback is defined as a technique used to align ai models with human values by using human feedback as a reward signal. Professionals in the field often use Reinforcement Learning from Human Feedback in conjunction with other technologies to build robust solutions. ...

Retrieval-Augmented Generation Definition An AI framework that retrieves facts from an external knowledge base to ground large language models (LLMs) on the most accurate, up-to-date information. Detailed Explanation Understanding Retrieval-Augmented Generation is crucial for mastering modern AI. It describes an ai framework that retrieves facts from an external knowledge base to ground large language models (llms) on the most accurate, up-to-date information. Professionals in the field often use Retrieval-Augmented Generation in conjunction with other technologies to build robust solutions. ...

Tokenization Definition The process of breaking down text into smaller units called tokens (words, subwords, or characters) for processing by AI models. Detailed Explanation In the world of Nlp, Tokenization is defined as the process of breaking down text into smaller units called tokens (words, subwords, or characters) for processing by ai models. Professionals in the field often use Tokenization in conjunction with other technologies to build robust solutions. Why Tokenization MattersFor developers and data scientists, mastering Tokenization unlocks new capabilities in model design. It is particularly relevant for optimizing performance and reducing costs. ...

Tool Use Definition The capability of an AI model to utilize external tools (like calculators, search engines, or APIs) to complete tasks. Detailed Explanation Understanding Tool Use is crucial for mastering modern AI. It describes the capability of an ai model to utilize external tools (like calculators, search engines, or apis) to complete tasks. At its core, Tool Use solves a specific problem in the AI landscape. Unlike traditional approaches, it leverages advanced algorithms to process data more efficiently. ...

TPU Definition Tensor Processing Unit, an AI accelerator application-specific integrated circuit (ASIC) developed by Google. Detailed Explanation TPU is a fundamental concept in Hardware that refers to tensor processing unit, an ai accelerator application-specific integrated circuit (asic) developed by google. At its core, TPU solves a specific problem in the AI landscape. Unlike traditional approaches, it leverages advanced algorithms to process data more efficiently. Why TPU MattersFor developers and data scientists, mastering TPU unlocks new capabilities in model design. It is particularly relevant for optimizing performance and reducing costs. ...

Transformer Definition A deep learning architecture introduced by Google in 2017 that uses self-attention mechanisms to weigh the significance of each part of the input data. Detailed Explanation Transformer is a fundamental concept in Architecture that refers to a deep learning architecture introduced by google in 2017 that uses self-attention mechanisms to weigh the significance of each part of the input data. The significance of Transformer cannot be overstated. As AI systems become more complex, mechanisms like this ensure scalability and accuracy. ...

Vector Database Definition A database designed to store and query vector embeddings for efficient similarity search. Detailed Explanation Understanding Vector Database is crucial for mastering modern AI. It describes a database designed to store and query vector embeddings for efficient similarity search. Professionals in the field often use Vector Database in conjunction with other technologies to build robust solutions. Why Vector Database MattersFor developers and data scientists, mastering Vector Database unlocks new capabilities in model design. It is particularly relevant for optimizing performance and reducing costs. ...

Zero-Shot Learning Definition The ability of a model to perform a task it wasn’t explicitly trained for. Detailed Explanation Understanding Zero-Shot Learning is crucial for mastering modern AI. It describes the ability of a model to perform a task it wasn't explicitly trained for. At its core, Zero-Shot Learning solves a specific problem in the AI landscape. Unlike traditional approaches, it leverages advanced algorithms to process data more efficiently. Applications of Zero-Shot LearningReal-world applications include advanced natural language processing, computer vision systems, and automated decision-making frameworks. ...