AI Terminology Simplified: A Plain-English Guide to Common AI Terms

AI Terminology Simplified: A Plain-English Guide to Common AI Terms

Artificial intelligence is frequently described using technical language that can obscure meaning rather than clarify it. Terms are reused across marketing, research, and product documentation, often with different implications depending on context.

This guide simplifies commonly used AI terminology and explains what these terms typically mean in real-world use.

Artificial Intelligence (AI)

Artificial intelligence is a broad field focused on building systems that perform tasks normally associated with human intelligence. These tasks may include language processing, perception, decision-making, and problem solving.

AI is an umbrella term that covers many different techniques and system designs.

Machine Learning (ML)

Machine learning is a subset of AI that enables systems to learn patterns from data rather than relying entirely on explicitly coded rules.

In practice, machine learning is used for tasks such as prediction, classification, recommendation, and forecasting.

Deep Learning

Deep learning is a subset of machine learning that uses neural networks with multiple layers. It is particularly effective for complex pattern recognition tasks such as image analysis, speech recognition, and natural language processing.

Not all machine learning involves deep learning, and many production systems use simpler models when appropriate.

Generative AI

Generative AI refers to systems that can create new content, such as text, images, code, or audio, based on patterns learned during training.

Generative AI does not retrieve prewritten responses. It produces outputs probabilistically and can generate content that is plausible but not always correct.

Large Language Models (LLMs)

Large language models are a type of generative AI trained on large volumes of text. They are designed to predict and generate sequences of words based on context.

LLMs are commonly used in chat interfaces, writing tools, and code assistants.

Training Data

Training data is the information used to teach an AI or machine learning model how to perform a task. The quality, scope, and representativeness of this data strongly influence model behavior.

Training data does not give a model knowledge in the human sense; it shapes statistical patterns.

Model

A model is the mathematical representation created during training. It contains parameters that determine how inputs are transformed into outputs.

When people refer to “using AI,” they are typically interacting with a deployed model through software.

Inference

Inference is the process of using a trained model to generate outputs from new inputs. This occurs after training and is what happens when a user interacts with an AI system.

Training and inference are distinct phases with different performance and cost considerations.

Prompt

A prompt is the input provided to a generative AI system. It may be a question, instruction, example, or combination of these elements.

Prompts influence outputs but do not guarantee specific results.

Hallucination

Hallucination is an informal term used to describe AI-generated outputs that are incorrect, unsupported, or fabricated, despite appearing confident or coherent.

This behavior reflects the probabilistic nature of generative models rather than intentional deception.

Bias

Bias refers to systematic patterns in AI outputs that reflect imbalances or assumptions present in training data or system design.

Bias is a property of systems and data, not intent, and must be addressed through evaluation and governance.

Fine-Tuning

Fine-tuning is the process of adapting a pre-trained model using additional, task-specific data. This can improve performance for particular use cases while leveraging an existing model foundation.

AI Agent

An AI agent is a system designed to pursue goals through multi-step decision-making and action. Agents often combine models, rules, tools, and feedback loops.

Not all AI systems are agents, and most deployed agents operate under constraints and supervision.

Why Terminology Matters

Clear terminology helps organizations:

Set realistic expectations

Scope projects accurately

Communicate across technical and non-technical teams

Evaluate risks and limitations

Misunderstood terms often lead to overpromising and underdelivering.

How to Use This Glossary

When encountering AI terminology:

Ask what the system actually does in practice

Distinguish between marketing language and technical function

Focus on inputs, outputs, and decision boundaries

Avoid assuming human-like intelligence

Precision in language supports better decisions.

Key Takeaways

AI terminology is often used inconsistently across contexts.

AI is a broad field; machine learning and generative AI are subsets.

Models learn statistical patterns, not understanding or intent.

Clear definitions reduce confusion and improve adoption outcomes.