From AI to Z: A Plain-Language Glossary of AI Terms for Editors

artificial intelligence

In my ongoing "Editors and AI" blog series, I've explored various aspects of artificial intelligence and its impact on editing. One stumbling block for many editors is simply understanding the terminology that gets thrown around in these discussions. Between industry newsletters mentioning AI, software companies adding AI features to familiar tools, and clients asking increasingly complex questions, it’s more important than ever to have a clear grasp of AI concepts.

I've created this straightforward glossary of AI terms specifically for editorial professionals. No jargon, no hype—just clear explanations of what these terms actually mean for your editing practice.

A

Artificial Intelligence (AI)

An umbrella term encompassing many different technologies that enable computers to perform tasks that typically require human intelligence. As I've discussed in part I of this series, AI is like using "vehicle" to describe everything from skateboards to cargo ships—it's too broad to be meaningful without context.

AI Bias

The tendency of AI systems to reflect and sometimes amplify biases present in their training data.

Example: Image generators creating primarily white male doctors when prompted for "doctor" images.

AI Effect

The phenomenon where once an AI technology becomes commonplace, people no longer consider it "AI." This helps explain why AI seems to be a constantly moving target.

Example: Calculators, spell-checkers, and Alexa and Siri were once considered cutting-edge AI; now they're just basic tools. For other examples, see this post.

AI-Enhanced Tools

Editorial software that combines traditional rule-based functionality with some AI capabilities. I explore these in detail in part II of this series.

Example: Microsoft Word 365's Editor feature uses machine learning (a type of AI) alongside traditional rule-based programming.

C

Chatbot

The interface you're likely using when you interact with AI tools like ChatGPT or Claude. While we often use terms like "ChatGPT" to refer to the entire AI system, technically a chatbot is just the conversational interface that lets you talk to the underlying AI model (the LLM).

Example: When you're typing questions to ChatGPT in its chat window, you're using a chatbot, but the actual "brains" behind the responses is GPT-4, the large language model doing all the work behind the scenes.

See also: Large Language Models (LLMs)

Context Confusion

AI's inability to fully understand the context or purpose of a text, leading to inappropriate suggestions. I discuss this and other limitations in part III.

Example: AI suggesting changes to technical terminology in a medical manuscript because it doesn't recognize that precise technical language is intentional in that context.

Context Window

How much text an AI model can "remember" and consider at one time. Different AI models have varying context window sizes—from a few thousand words to an entire book.

F

False Confidence

AI's tendency to present information with absolute certainty even when it's incorrect.

Example: AI producing a detailed explanation of grammar rules that don't actually exist, written in an authoritative tone.

See also: Hallucination

G

Generative AI

A subset of machine learning that can create new content (text, images, audio) by analyzing patterns in its training data. This is the type of AI that's been making headlines recently. I discuss how generative AI actually works in part III of this series.

Example: ChatGPT writing a blog post or Midjourney creating an image from a text description.

H

Hallucination

When AI confidently generates false information, citations, or facts. This is particularly problematic for editors who rely on accuracy, as I explain in part III.

Example: ChatGPT citing a nonexistent Chicago Manual of Style rule or making up statistics.

See also: False Confidence

Hybrid Tools

Editorial software that combines traditional rule-based functionality with some AI capabilities. Learn more about which editing tools use AI and which don't in part II of my series.

Example: Microsoft Word 365's Editor feature uses machine learning alongside its traditional programming.

L

Large Language Models (LLMs)

Sophisticated AI systems trained on massive amounts of text that can understand and generate human-like language.

Example: The models behind ChatGPT, Claude, and Google Gemini.

See also: Chatbot

M

Machine Learning

A type of AI where systems learn from patterns in data and improve over time, rather than following explicitly programmed rules. I discuss the relationship between AI and machine learning in part I of my series.

Example: Grammarly's suggestions become more tailored to your writing style the more you use it.

N

Natural Language Processing (NLP)

This type of AI helps computers understand human language. Tools like Grammarly and Microsoft Word's Editor use NLP to suggest ways to make your writing better. I place this in what I call the “AI family tree” in part I.

O

Overcorrection

AI's tendency to make more changes than requested, often rewriting text completely when asked for light edits. This is one of several limitations I explore in part III.

Example: Asking an AI to "lightly copyedit" a paragraph, only to have it completely rewrite the entire passage.

P

Prompt

The input text given to an AI system that tells it what to generate or respond to.

Example: "Explain why em dashes are different from en dashes in CMOS style."

R

Response/Output

The content generated by an AI system based on your prompt.

Example: The text ChatGPT produces when you ask it a question.

Rule-Based Systems

Traditional software that follows explicit, pre-programmed rules without learning or adapting. You can learn more about the difference between rule-based systems and AI in part II of my series.

Example: Traditional spell-checkers and PerfectIt operate using rule-based systems, not AI.

T

Training Data Cutoff

The point after which an AI system hasn't been trained on new information.

Example: If an AI's training data cutoff was December 2023, it won't have reliable information about events or style guide updates that happened after that date.

Understanding these terms can help you navigate conversations about AI in editing, evaluate new tools more effectively, and make informed decisions about if and how to incorporate AI into your editorial practice. While the technology is evolving rapidly, having this baseline knowledge will allow you to approach AI with both curiosity and critical thinking.

For more in-depth discussions of AI and its impact on editing, check out the rest of my "Editors and AI" series:

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