AI and Prompt Engineering Glossary: The Most Important Terms You Need to Know

Welcome to the IdentityPrompts.com Glossary – our complete, beginner-friendly reference for the most important terms in artificial intelligence, prompt engineering, machine learning, and large language models. This SEO-optimized glossary covers the top searched keywords people look for when learning how modern AI works.

Use this as your quick guide whenever you encounter new AI terminology or want to understand how different concepts fit together.

A

AI (Artificial Intelligence)

The broad field of creating machines or systems that can perform tasks that usually require human intelligence, such as problem-solving, learning, reasoning, and language understanding.

AI Agent

A system that uses prompts, tools, and reasoning steps to autonomously take actions, complete tasks, or make decisions with minimal human input.

Algorithm

A set of instructions or rules an AI model follows to make predictions, analyze data, or generate content.

B

Bias (AI Bias)

Unintended or unfair patterns in AI-generated output caused by imbalanced training data or flawed design. Bias can appear in language, recommendations, or decision-making.

Benchmarking (AI Evaluation)

The process of testing AI models using standardized datasets or tasks to measure their accuracy, speed, or reasoning capabilities.

C

ChatGPT

A popular AI chatbot built on OpenAI’s large language models. It’s widely used for prompting, writing, coding, research, and automation.

Claude

An advanced AI assistant created by Anthropic, known for strong reasoning, safety, and long-context performance.

Computer Vision

A branch of AI that allows computers to understand and analyze images and video.

Context Window

The maximum amount of text or information an AI model can process at once. Larger context windows allow models to understand longer conversations or documents.

D

Deep Learning

A type of machine learning that uses multi-layered neural networks to detect patterns and make predictions from large amounts of data.

Dataset

A collection of text, images, audio, or other data used to train AI or machine learning models.

E

Embedding

A numerical representation of text, images, or other data that helps AI models understand relationships, similarity, and meaning.

Ethical AI

The study and practice of ensuring AI systems are safe, fair, transparent, and respectful of user privacy and human values.

F

Fine-Tuning

A method of customizing an AI model by training it further on specific datasets to improve performance on targeted tasks.

Foundation Model

A large-scale AI model trained on massive datasets that can be adapted to many tasks, such as ChatGPT, Claude, Gemini, and Llama.

G

Generative AI (GenAI)

AI that creates new content – including text, images, audio, and video. Examples include ChatGPT, DALL·E, Midjourney, and Sora.

GPT (Generative Pretrained Transformer)

A family of AI language models created by OpenAI that generate human-like text using transformer architecture.

H

Hallucination (AI Hallucination)

When an AI confidently generates incorrect, inaccurate, or fabricated information. This is a common challenge with large language models.

Hyperparameters

Settings used during AI model training that affect performance but are not learned by the model (e.g., learning rate, batch size).

I

Inference

The process of an AI model generating predictions or responses based on input data or prompts.

Instruction Tuning

A technique where models are trained specifically to follow user instructions and prompts more accurately.

L

Large Language Model (LLM)

A type of AI model trained on massive text datasets capable of generating human-like language, answering questions, and performing reasoning tasks.

LLM Prompting

The process of writing structured instructions to guide large language models into producing accurate and useful responses.

Latency

The time it takes for an AI model to generate a response after receiving a prompt.

M

Machine Learning (ML)

A subset of AI where models learn patterns from data instead of being explicitly programmed to perform tasks.

Model Parameters

The internal values an AI adjusts during training to make accurate predictions.

Multimodal AI

AI systems that understand or generate multiple types of data, such as text, images, audio, and video.

N

Natural Language Processing (NLP)

The field of AI focused on enabling computers to understand, interpret, and generate human language.

Neural Network

A system of algorithms inspired by the human brain that helps AI models recognize patterns and learn from data.

O

OpenAI

A leading AI research company behind models like GPT, ChatGPT, DALL·E, and Sora.

Output Format

The structure you want the AI to follow when responding for example, bullet points, lists, scripts, tables, or paragraphs.

P

Parameter Count

The number of trainable parameters in a model. Larger models often perform better but require more compute.

POML (Prompt Orchestration Markup Language)

A structured way of writing and organizing prompts to make AI models more consistent and predictable. It’s increasingly used in advanced prompt engineering.

Prompt

A written instruction given to an AI that guides it to produce a specific answer, task, or output.

Prompt Engineering

The science and strategy of designing effective prompts that lead to accurate, helpful, and reliable AI responses.

Prompt Template

A reusable structure or formula for prompting AI, often used for writing, business, productivity, or development tasks.

R

Reinforcement Learning (RL)

A machine learning method where models learn by interacting with an environment and receiving rewards or penalties.

RAG (Retrieval-Augmented Generation)

A technique that allows AI models to pull information from external sources or documents before generating a response, improving accuracy.

S

Safety Alignment

Techniques used to ensure AI behaves safely, avoids harmful content, and follows human values.

Semantic Search

A search method that understands meaning rather than exact keywords, often powered by embeddings.

Structured Output

A technique where AI is instructed to respond in organized formats like JSON, tables, or spreadsheets.

T

Transformer Model

A groundbreaking deep learning architecture behind most modern AI models, including GPT, Claude, and Llama.

Temperature

A setting that controls how creative or deterministic an AI model’s output is. Higher temperature = more creative; lower temperature = more precise.

U

Use Case

A specific practical application for AI, such as writing blog posts, generating code, summarizing documents, or creating workflows.

V

Vector Database

A type of database designed to store and retrieve embeddings. Used in RAG systems and semantic search applications.

Vision Model

An AI system trained to interpret visual input such as images, diagrams, or video.

W

Workflow Automation

Using AI tools and structured prompts to automate repetitive tasks, streamline processes, and improve productivity.