AI Glossary
The terms that keep coming up in meetings, articles, and proposals — explained clearly. No unnecessary jargon.
44 terms · updated May 2026
Adversarial Attacks
Techniques used to fool AI models using manipulated data, causing them to produce incorrect results.
AGI
Artificial General Intelligence — A type of artificial intelligence capable of performing any intellectual task that a human can do. Does not yet exist in practice.
AI Agents
Systems that can understand information, reason, and execute actions autonomously.
Artificial Intelligence (AI)
Field of technology focused on creating machines capable of performing tasks that normally require human intelligence, such as learning, reasoning, or making decisions.
Chain-of-thought Prompting
Technique that guides a model to solve problems step by step, showing its intermediate reasoning.
Conversational Agents
AI designed to interact with people through natural language, understanding intent and context.
Data Accessibility
Availability of data in the right format and location to be used by AI models.
Data Ingestion and Preparation
Process of collecting, cleaning, and transforming raw data into a format suitable for use by AI models.
Data Quality
How correct, complete, consistent, and useful the data is.
Deep Learning
Type of machine learning that uses neural networks with many layers to detect complex patterns.
Ethical AI
Use of artificial intelligence in a responsible way, avoiding harm or bias.
Fine-tuning
Process of adapting an already-trained AI model to a specific task using new data.
Foundation Models
AI models trained on large volumes of data that can be adapted to multiple tasks.
Generative AI
Type of AI capable of creating new content, such as text, images, code, or audio.
Google Gems
Customized AI assistants that help with specific tasks within a defined context.
GPUs
Graphics Processing Units. Hardware originally designed for graphics, now widely used to train AI models due to their parallel processing capacity.
Grounding
Process of connecting AI responses to real or verifiable sources to increase accuracy.
Humans in the Loop (HITL)
Approach where humans supervise or intervene in AI systems to improve results or avoid errors.
Hypercomputer
Infrastructure that connects multiple computers (with GPUs/TPUs) to process large AI workloads.
Labeled Data
Data that has additional information (such as categories or names) that helps train models.
Large Language Models (LLMs)
AI models specialized in understanding and generating human language (such as ChatGPT or Claude).
Machine Learning (ML)
Subfield of AI where machines learn from data without being explicitly programmed.
ML Learning Approaches
Types of machine learning: Supervised (uses labeled data), Unsupervised (finds patterns), Reinforcement (learns from feedback).
Model Deployment
Process of putting a trained model into production so it can be used.
Model Management
Administration of the model lifecycle: monitoring, updating, and maintenance.
Model Training
Process of teaching a model using data.
Prompt Chaining
Use of multiple prompts connected to each other to solve more complex tasks.
Prompt Engineering
Art of designing instructions (prompts) to get better results from AI models.
ReAct (Reason + Act)
Framework that allows a model to reason and take actions step by step when facing a problem.
Reasoning Loop
Internal cycle of an AI agent where it analyzes information, reasons, and decides what to do next.
Reinforcement Learning
Type of learning where the model improves through trial and error, receiving rewards or penalties.
Responsible AI
Principles and practices to ensure that AI is safe, fair, and trustworthy.
Retrieval-Augmented Generation (RAG)
Technique that combines external information retrieval with response generation to improve accuracy.
Role Prompting
Technique where a role is assigned to the model to guide its behavior.
SAIF (Secure AI Framework)
Framework for developing and operating AI systems securely.
Secure AI
Practices to protect AI systems from attacks or malicious use.
Structured Data
Data organized in clear formats, such as tables or databases.
Structured vs Unstructured Data
Structured data is organized and easy to analyze; unstructured data requires more advanced techniques.
Supervised Learning
Training with labeled data where the model learns the relationship between input and output.
TPUs (Tensor Processing Units)
Chips designed specifically for artificial intelligence tasks, developed by Google.
Unlabeled Data
Data without classification or labels, used in unsupervised learning.
Unstructured Data
Data without a defined format, such as text, images, or videos.
Unsupervised Learning
Model that finds patterns in data without labels.
Workflow Agents
AI designed to automate tasks and processes within a workflow.
The glossary grows. If there's a concept you use that isn't here yet, write to us and we'll add it.
Suggest a term →