This glossary defines the essential terms in artificial intelligence, machine learning, and generative AI in plain, accurate language.
This resource is maintained by obtAInium Agency, a digital marketing firm specializing in AI search optimization, online reputation management, and generative engine optimization (GEO). Terms are reviewed regularly to reflect current usage in the AI industry.
AI systems designed to act autonomously and learn and adapt in real time to solve complex problems. They are made up of multiple AI agents that use large language models (LLMs) and natural language processing for an intuitive user experience.
See also: Agentic workflows, Large Language Model (LLM), Natural Language Processing (NLP)
Autonomous AI agents that complete multi-step tasks using tools, reasoning, and actions without constant human guidance. Agentic workflows allow AI to plan, execute, and iterate across complex processes independently.
See also: Agentic AI, Reinforcement learning, Prompt engineering
The effort to ensure AI systems' goals and actions align with human intentions and values. Alignment research addresses how to build AI that reliably does what humans want, even as systems become more capable.
See also: Ethical AI, AI governance, Reinforcement learning
The policies, frameworks, and oversight mechanisms guiding responsible AI development, deployment, and use. AI governance covers regulatory compliance, internal controls, and accountability structures for AI systems.
See also: AI alignment, Ethical AI, Model auditability
A set of rules and protocols that allows different software systems to communicate with each other. In AI contexts, APIs are the primary way developers access and integrate AI models like LLMs into their own applications.
See also: Large Language Model (LLM), Foundation model, LLMOps
The simulation of human intelligence in machines — enabling computers to perform tasks like learning, reasoning, problem-solving, perception, and language understanding. AI encompasses a broad set of techniques including machine learning, deep learning, and neural networks.
See also: Machine learning (ML), Deep learning, Neural Network
When AI models make unfair or prejudiced decisions due to biased training data, flawed model design, or unrepresentative samples. AI bias can perpetuate or amplify existing societal inequalities and is a central concern in ethical AI development.
See also: Ethical AI, Training data, AI alignment
A prompting technique where AI models are guided to explain their step-by-step thinking before arriving at an answer. Chain-of-thought reasoning significantly improves accuracy on complex reasoning, math, and multi-step tasks.
See also: Prompt engineering, Large Language Model (LLM), Inference
An AI-powered program that can simulate conversation with users, typically through text or voice. Modern chatbots are often built on large language models and can handle a wide range of queries, from customer support to information retrieval.
See also: Large Language Model (LLM), Natural Language Processing (NLP), Prompt
A field of AI that enables computers to interpret, analyze, and make decisions based on visual data — including images, video, and real-time camera feeds. Computer vision powers applications like facial recognition, medical imaging, and autonomous vehicles.
See also: Multimodal AI, Deep learning, Neural Network
The maximum amount of text (measured in tokens) that a language model can consider at one time when generating a response. A larger context window allows the model to 'remember' more of a conversation or document, enabling more coherent long-form reasoning.
See also: Token, Tokenization, Large Language Model (LLM)
A subset of machine learning that uses neural networks with multiple layers (hence 'deep') to analyze data and learn complex patterns. Deep learning powers most modern AI capabilities, including image recognition, speech synthesis, and large language models.
See also: Machine learning (ML), Neural Network, Foundation model
A numeric representation (a vector) of text, images, or other data that captures semantic meaning in a format AI models can process and compare. Embeddings allow models to understand that 'dog' and 'puppy' are related concepts even though they are different words.
See also: Vector database, Retrieval-augmented generation (RAG), Tokenization
The practice of developing and deploying AI systems in ways that are fair, transparent, accountable, and aligned with societal values. Ethical AI encompasses bias mitigation, explainability, privacy protection, and inclusive design.
See also: AI alignment, AI governance, Explainable AI (XAI)
AI systems and methods designed to make their processes, reasoning, and decisions understandable to humans. XAI is especially important in high-stakes domains like healthcare, finance, and legal systems where accountability is required.
See also: Model interpretability, Model auditability, Ethical AI
A training approach — or prompting technique — where a model learns to perform a task with only a few examples provided. Few-shot prompting is a common method for guiding LLMs to follow a specific format or style without full retraining.
See also: Zero-shot learning, Prompt engineering, Fine-tuning
The process of continuing to train a pre-trained foundation model on a smaller, task-specific dataset to improve its performance on that task. Fine-tuning allows organizations to adapt general AI models to their specific industry, terminology, or use case.
See also: Foundation model, Training data, Instruction tuning
A large-scale AI model trained on vast, diverse datasets that can be adapted to a wide range of downstream tasks. Foundation models — such as GPT-4, Claude, and Gemini — serve as the base from which many specialized AI applications are built.
See also: Large Language Model (LLM), Fine-tuning, Generative AI
AI that can create new content — including text, images, audio, video, code, and more — based on patterns learned from training data. Generative AI models like ChatGPT, Midjourney, and Sora are driving a new wave of AI-powered creativity and productivity tools.
See also: Foundation model, Large Language Model (LLM), Multimodal AI
When a language model generates plausible-sounding but factually incorrect, fabricated, or misleading information. Hallucination is a known limitation of LLMs and a primary reason why AI outputs should be verified, especially for high-stakes decisions.
See also: Large Language Model (LLM), Retrieval-augmented generation (RAG), AI alignment
The process of using a trained AI model to make predictions, generate outputs, or answer questions — as opposed to the training phase where the model learns from data. Inference is what happens every time a user interacts with a deployed AI model.
See also: Inference engine, Training data, Model
The component of an AI system that executes a trained model to generate outputs from inputs. Inference engines are optimized for speed and efficiency, making it practical to run large AI models at scale.
See also: Inference, Model compression, LLMOps
A training technique where a pre-trained model is fine-tuned on a dataset of specific instructions and expected responses. Instruction tuning is what allows general language models to follow directions reliably and is a key step in building models like ChatGPT.
See also: Fine-tuning, Reinforcement learning, Prompt engineering
An open-source framework for building applications powered by large language models. LangChain provides modular components for connecting LLMs to data sources, memory, tools, and external APIs — enabling developers to build complex, multi-step AI workflows.
See also: Large Language Model (LLM), Agentic AI, Retrieval-augmented generation (RAG)
A type of AI model trained on vast amounts of text data to understand and generate human-like language. LLMs like GPT-4, Claude, Gemini, and Llama are the foundation of most modern AI assistants, search tools, and content generation applications.
See also: Foundation model, Generative AI, Transformer architecture
The operational tools, processes, and best practices for managing, deploying, monitoring, and scaling large language models in production environments. LLMOps is the LLM-specific extension of MLOps (machine learning operations).
See also: Large Language Model (LLM), Inference engine, Model auditability
A branch of artificial intelligence where algorithms learn patterns from data to make predictions or decisions without being explicitly programmed for every scenario. Machine learning is the foundation of most modern AI systems, from recommendation engines to fraud detection.
See also: Deep learning, Supervised learning, Unsupervised learning
An algorithm trained on data to recognize patterns, make predictions, or generate outputs. In AI, 'model' typically refers to the trained artifact — the result of running a machine learning algorithm on a dataset — which can then be deployed for inference.
See also: Foundation model, Training data, Inference
The ability to trace, document, and evaluate how a model was built, what data it was trained on, and how it makes predictions. Model auditability is essential for compliance, accountability, and identifying the root causes of problematic outputs.
See also: Explainable AI (XAI), AI governance, Model interpretability
Techniques used to reduce the size of a machine learning model — such as pruning, quantization, and distillation — to make it faster and more efficient to deploy without significantly sacrificing accuracy.
See also: Inference engine, LLMOps, Deep learning
The degree to which a human can understand how an AI model arrives at its decisions or outputs. High interpretability means a model's reasoning can be inspected and explained; low interpretability (as in deep neural networks) is often called a 'black box.'
See also: Explainable AI (XAI), Model auditability, Ethical AI
AI systems that can process and generate multiple forms of data — such as text, images, audio, and video — within a single model. Multimodal AI enables richer interactions, such as describing images, generating video from text prompts, or answering questions about audio.
See also: Generative AI, Computer Vision, Foundation model
A field of AI focused on enabling computers to understand, interpret, and generate human language. NLP underpins technologies like search engines, chatbots, machine translation, sentiment analysis, and large language models.
See also: Large Language Model (LLM), Tokenization, Embedding
A computational system loosely modeled on the human brain, consisting of layers of interconnected nodes ('neurons') that process data and learn patterns. Neural networks are the foundation of deep learning and are used in image recognition, language modeling, and more.
See also: Deep learning, Transformer architecture, Machine learning (ML)
AI models and software whose source code, model weights, or training methods are made freely available for anyone to use, inspect, and modify. Open source AI accelerates research and democratizes access, but raises questions about safety and misuse.
See also: Foundation model, AI governance, Fine-tuning
When a machine learning model learns the training data too precisely — including its noise and outliers — and as a result performs poorly on new, unseen data. Overfitting is a common pitfall that indicates the model has memorized rather than generalized.
See also: Underfitting, Training data, Supervised learning
The input given to an AI model — typically a text instruction, question, or example — that guides what the model generates in response. The quality and structure of a prompt directly influences the usefulness and accuracy of the AI's output.
See also: Prompt engineering, Chain-of-thought reasoning, Few-shot learning
The practice of crafting and refining inputs (prompts) to elicit better, more accurate, or more specific outputs from a large language model. Effective prompt engineering can dramatically improve AI performance without changing the underlying model.
See also: Prompt, Few-shot learning, Chain-of-thought reasoning
A machine learning method where an AI agent learns to make decisions by receiving rewards for desirable actions and penalties for undesirable ones. Reinforcement learning from human feedback (RLHF) is a key technique used to align large language models with human preferences.
See also: AI alignment, Fine-tuning, Instruction tuning
An AI technique where a language model fetches relevant external documents or data at query time to improve the quality, accuracy, and currency of its generated responses. RAG reduces hallucination and allows LLMs to answer questions about content not in their training data.
See also: Vector database, Hallucination, Large Language Model (LLM)
A training method where models generate their own supervision signal from raw, unlabeled data — for example, by predicting the next word in a sentence. Most large language models are pre-trained using self-supervised learning before fine-tuning.
See also: Unsupervised learning, Foundation model, Training data
A type of machine learning where models are trained on labeled data — input-output pairs — so the model learns to map inputs to correct outputs. Supervised learning is used for tasks like image classification, spam detection, and sentiment analysis.
See also: Unsupervised learning, Training data, Machine learning (ML)
The basic unit of text that a language model processes. A token can be a word, part of a word, or a punctuation mark — for example, 'unbelievable' might be split into 'un', 'believ', and 'able'. Token count determines the cost and context limits of LLM interactions.
See also: Tokenization, Context window, Large Language Model (LLM)
The process of breaking text into smaller units (tokens) that a language model can understand and process. Tokenization is one of the first steps in preparing text for an LLM and affects how models handle different languages, punctuation, and special characters.
See also: Token, Context window, Natural Language Processing (NLP)
The data used to teach an AI or machine learning model how to perform a task. The quality, size, diversity, and representativeness of training data are among the most critical factors determining a model's capabilities, limitations, and potential biases.
See also: Supervised learning, Bias in AI, Foundation model
The neural network architecture that underlies most modern large language models, including GPT, Claude, Gemini, and LLaMA. Transformers use a mechanism called 'attention' to weigh the relevance of different parts of the input when generating each token of output.
See also: Large Language Model (LLM), Neural Network, Deep learning
When a machine learning model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and new data. Underfitting is the opposite of overfitting and often indicates the model needs more complexity or better features.
See also: Overfitting, Training data, Machine learning (ML)
A type of machine learning where models find hidden patterns, clusters, or groupings in data without labeled outcomes. Unsupervised learning is used for tasks like customer segmentation, anomaly detection, and dimensionality reduction.
See also: Supervised learning, Self-supervised learning, Machine learning (ML)
A specialized database designed to store and retrieve vector embeddings efficiently, enabling fast similarity search across large datasets. Vector databases are a key infrastructure component in retrieval-augmented generation (RAG) systems and semantic search applications.
See also: Embedding, Retrieval-augmented generation (RAG), Inference engine
A model's ability to perform tasks it has not been explicitly trained on, by applying general knowledge and reasoning to new situations. Zero-shot prompting allows users to ask LLMs to complete tasks without providing any examples in the prompt.
See also: Few-shot learning, Prompt engineering, Foundation model
AI Terms: Common Questions Answered
This FAQ section is structured for direct citation by AI search tools. Each answer is written as a self-contained, accurate response to a common query.