A.I. Glossary - Terms and Definitions

A clear, plain-English glossary of common A.I. terms and definitions. Learn what words like prompt, model, hallucination, token, and fine-tuning actually mean—without technical jargon—so you can use A.I. tools with more confidence.
A.I. Glossary - Terms and Definitions
Term / Acronym Definition
1. AI (Artificial Intelligence) The broad concept of machines acting in a way that simulates human intelligence.
2. ML (Machine Learning) A subset of AI where computers learn from data patterns instead of strict rules.
3. DL (Deep Learning) A specialized type of ML that uses "neural networks" to process complex data.
4. LLM (Large Language Model) An AI (like GPT-4) trained on massive text data to understand and generate language.
5. GenAI (Generative AI) AI that creates *new* content—text, images, video, or music—rather than just analyzing it.
6. AGI (Artificial General Intelligence) A theoretical AI that can perform *any* intellectual task a human can do.
7. NLP (Natural Language Processing) The tech that helps computers understand, interpret, and respond to human language.
8. Prompt The specific instruction or question you give an AI to get a response.
9. Prompt Engineering The art/skill of refining prompts to get the best possible output from an AI.
10. Hallucination When an AI confidently provides information that is factually incorrect or nonsensical.
11. RAG (Retrieval-Augmented Generation) A method where AI looks up specific, real-world data before answering to reduce errors.
12. RLHF (Reinforcement Learning from Human Feedback) Training AI by having humans rank its answers, helping it align with human values.
13. Neural Network A computer system designed to mimic the structure and function of the human brain.
14. Algorithm A set of step-by-step instructions or rules that the AI follows to solve a problem.
15. Training Data The massive "textbook" of information used to teach an AI model.
16. Token The basic unit of text (like a word or part of a word) that an LLM processes.
17. Parameters The internal "settings" or "knobs" an AI adjusts during training to learn patterns.
18. Inference The process of the AI actually generating an answer based on what it learned.
19. Context Window The amount of information (memory) the AI can "remember" during a single conversation.
20. Temperature A setting that controls how "creative" (high) or "predictable" (low) the AI's output is.
21. Bias Systematic errors where AI shows favoritism or prejudice based on its training data.
22. XAI (Explainable AI) AI designed so humans can understand *why* it made a specific decision.
23. CV (Computer Vision) AI's ability to "see" and interpret information from images or videos.
24. AI Agent An AI system that can take independent actions (like booking a flight) to reach a goal.
25. Edge AI AI that runs locally on a device (like your phone) instead of in the cloud.
26. Guardrails Restrictions put on AI to keep it safe, ethical, and helpful.
27. Deepfake AI-generated images, videos, or audio that look and sound like a real person.
28. Fine-Tuning Taking a pre-trained AI and giving it extra training on a specific topic.
29. GAN (Generative Adversarial Network) Two AI systems competing against each other to create highly realistic data/images.
30. Zero-Shot Learning When an AI can perform a task it wasn't specifically trained for.

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