Bluehost AI- AI Glossary of Key Terms and Definitions
Artificial intelligence terminology can be confusing, especially with new tools and features being introduced all the time. This article defines the most common AI industry terms you'll encounter, from foundational concepts like machine learning and large language models to more specialized terms related to AI agents, content retrieval, and knowledge management.
Use this glossary as a quick reference whenever you come across an unfamiliar term in our AI-related product documentation or support articles.
Core AI Concepts
| Term | Definition |
|---|---|
| Artificial Intelligence (AI) | The broad field of building systems that perform tasks typically requiring human intelligence, such as reasoning, perception, and language understanding. |
| Machine Learning (ML) | A subset of AI where systems learn patterns from data rather than following explicitly programmed rules. |
| Deep Learning | A subset of ML using multi-layered neural networks to model complex patterns, especially effective for images, audio, and text. |
| Neural Network | A computing structure loosely inspired by the brain, made of layers of interconnected nodes ("neurons") that transform input data into output predictions. |
| Algorithm | A defined set of steps or rules a system follows to solve a problem or complete a task. |
| Model | The output of a training process — a set of learned parameters that can make predictions or generate content based on new input. |
| Training | The process of adjusting a model's internal parameters using data so it improves at a task. |
| Inference | The process of using a trained model to generate a prediction or output on new, unseen input. |
Language Models
| Term | Definition |
|---|---|
| Large Language Model (LLM) | A model trained on vast amounts of text to understand and generate human language (e.g., GPT, Claude, Gemini). |
| Foundation Model | A large model trained on broad data that can be adapted to many downstream tasks, rather than built for one narrow purpose. |
| Token | A chunk of text (word, part of a word, or character) that a language model processes as its basic unit of input/output. |
| Context Window | The maximum amount of text (measured in tokens) a model can consider at once when generating a response. |
| Prompt | The input text or instructions given to a model to elicit a response. |
| Prompt Engineering | The practice of crafting inputs to get more accurate, useful, or reliable outputs from a model. |
| System Prompt | Instructions given to a model before a conversation begins, setting behavior, tone, or constraints for the interaction. |
| Fine-tuning | Further training a pre-trained model on a smaller, specific dataset to specialize its behavior for a particular task or domain. |
| Hallucination | When a model generates information that sounds plausible but is factually incorrect or fabricated. |
| Temperature | A setting that controls the randomness of a model's output; lower values produce more predictable text, higher values produce more varied/creative text. |
Architecture & Training Techniques
| Term | Definition |
|---|---|
| Transformer | The neural network architecture underlying most modern LLMs, which uses "attention" to weigh the relevance of different parts of the input. |
| Attention Mechanism | A technique that allows a model to focus on the most relevant parts of the input when producing each part of the output. |
| Parameters | The internal numerical values a model learns during training; often used as a rough proxy for model size/capability (e.g., "70 billion parameters"). |
| Embedding | A numerical vector representation of text, images, or other data that captures semantic meaning, allowing similar items to be mathematically compared. |
| Vector Database | A database optimized for storing and searching embeddings by similarity, commonly used to power semantic search and RAG systems. |
| RAG (Retrieval-Augmented Generation) | A technique where a model retrieves relevant external documents or data at query time and uses them to ground its response, reducing hallucination. |
| RLHF (Reinforcement Learning from Human Feedback) | A training method where human preferences are used to guide a model toward more helpful, accurate, or aligned outputs. |
| Supervised Fine-Tuning (SFT) | Fine-tuning a model on labeled example input/output pairs to teach specific behaviors. |
Agents & Tool Use
| Term | Definition |
|---|---|
| AI Agent | A system that uses a model to autonomously plan and execute multi-step tasks, often by calling tools or external systems. |
| Tool Use / Function Calling | A model's ability to invoke external tools, APIs, or functions to retrieve information or perform actions beyond generating text. |
| Orchestration | The coordination of multiple models, tools, or steps to complete a complex workflow. |
| Multi-Agent System | An architecture where multiple AI agents interact or collaborate, each often specialized for a sub-task. |
| MCP (Model Context Protocol) | An open standard that lets AI models connect to external data sources and tools in a consistent way. |
Evaluation & Safety
| Term | Definition |
|---|---|
| Alignment | The effort to ensure an AI system's behavior matches human intentions and values. |
| Bias | Systematic skew in a model's outputs, often stemming from imbalances or patterns in its training data. |
| Guardrails | Rules, filters, or systems put in place to constrain a model's outputs and prevent harmful, unsafe, or off-policy behavior. |
| Benchmark | A standardized test or dataset used to measure and compare model performance on specific tasks. |
| Red Teaming | Deliberately probing a model with adversarial inputs to find weaknesses, vulnerabilities, or unsafe behaviors before deployment. |
| Explainability / Interpretability | The degree to which a model's decision-making process can be understood by humans. |
| Ground Truth | Verified, correct data used as a reference point to evaluate model accuracy. |
Data & Infrastructure
| Term | Definition |
|---|---|
| Training Data | The dataset used to teach a model patterns, typically consisting of massive amounts of text, images, or other content. |
| Dataset | A structured collection of data used for training, fine-tuning, or evaluating a model. |
| GPU (Graphics Processing Unit) | Specialized hardware widely used to run the parallel computations required for training and running AI models efficiently. |
| Latency | The time delay between sending a request to a model and receiving a response. |
| Throughput | The volume of requests or tokens a system can process in a given time period. |
| API (Application Programming Interface) | A defined way for software to interact with a model or service, typically used by developers to integrate AI into applications. |
AI Content Operations & Knowledge Management
| Term | Definition |
|---|---|
| Knowledge Base (KB) | A structured repository of articles, guides, and reference content designed to help users or support agents find answers. |
| Chunking | Splitting a document into smaller pieces so it can be embedded and retrieved efficiently in a RAG or search pipeline. |
| Structured Metadata | Tagged, machine-readable data (categories, keywords, product SKUs, etc.) attached to content to improve searchability and AI retrieval. |
| Taxonomy | A hierarchical system for classifying and organizing content or products so it can be consistently tagged, searched, and retrieved. |
| Semantic Search | Search that matches meaning and intent rather than exact keywords, typically powered by embeddings. |
| Content Sanitization | The process of cleaning and reformatting source content (removing formatting artifacts, jargon, or unsafe text) so it's suitable for downstream AI or voice use. |
| AEO (Answer Engine Optimization) | Optimizing content so it is easily surfaced and cited by AI-driven answer engines and chatbots, rather than only traditional search engines. |
| Structured Output | A model response formatted to a predictable schema (such as JSON) so it can be reliably parsed by downstream systems. |
| JSON Schema | A formal specification describing the expected structure and fields of a JSON output, often used to constrain model responses. |
| Voice Script / TTS Output | Content rewritten specifically for text-to-speech delivery, adjusted for pacing, pronunciation, and spoken clarity rather than reading on a page. |
| Gap Analysis | A systematic comparison between existing content and what's needed, used to identify missing KB articles or coverage holes. |
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