Glossary
Key AI Terms & Definitions
📚 This glossary contains essential AI terminology used throughout the book. Each term is explained in clear, accessible language to help you build your AI vocabulary.
Artificial Intelligence (AI)
The simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, and self-correction.
Large Language Model (LLM)
AI systems trained on vast amounts of text data to understand and generate human-like text. Examples include GPT-4, Claude, and Gemini.
Machine Learning
A subset of AI that enables systems to learn and improve from experience without being explicitly programmed. The system learns patterns from data.
Natural Language Processing (NLP)
The branch of AI that helps computers understand, interpret, and generate human language in a way that is both meaningful and useful.
Prompt Engineering
The practice of designing and refining inputs (prompts) to get desired outputs from AI models. An essential skill for effective AI interaction.
Neural Network
A computing system inspired by biological neural networks that constitute animal brains. Used in deep learning and AI systems.
Training Data
The dataset used to teach an AI model how to perform tasks. The quality and quantity of training data significantly impacts model performance.
Fine-tuning
The process of taking a pre-trained model and adapting it for a specific task or domain by training it on specialized data.
Hallucination
When an AI model generates information that sounds plausible but is actually incorrect or fabricated. A key limitation to be aware of.
Token
The basic unit of text that AI models process. A token can be a word, part of a word, or punctuation. Most models have token limits.
📖 This is a preview of the Glossary section. The complete book includes 100+ AI terms with detailed definitions, examples, and cross-references to help you master the vocabulary of artificial intelligence.
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