AI Glossary: From Curious to Confident, One Term at a Time

We get it. AI can feel like learning a new language, but once you get a handle on the terms, it’s a lot less intimidating (and way more empowering). Whether you're just flirting with AI or already building something big, here’s your cheat sheet to sounding (and feeling) like the most informed person in the room.

Let’s decode the buzzwords, shall we?

📚 Start Here: Core Concepts

Artificial Intelligence (AI): Technology that mimics human thinking and decision-making. The brains behind the bots.

Computer Vision: Allows machines to interpret images and video. When AI gets eyes.

Deep Learning: A powerful form of Machine Learning (ML) using layered neural networks. Machine learning's moody, complex sibling. More on neural networks below…

Generative AI: AI that creates original content, such as text, images, or audio. The creative side of AI: think text responses, custom art, or AI voiceovers.

Large Language Model (LLM): A type of generative AI trained on large text datasets (e.g., ChatGPT, Claude). Massive brainiac models with serious reading habits.

Machine Learning (ML): A subset of AI where systems learn from data rather than explicit programming. Think: less micromanaging, more self-starter energy.

Multimodal AI: AI that understands and combines more than one type of input (e.g., text + images). A multitasker that gets your references and your selfies.

Natural Language Processing (NLP): Enables machines to understand and generate human language. It’s how AI reads, writes, and chats like a human.

Neural Network: A system of algorithms modeled after how the human brain works. Cue the science fiction soundtrack.

RAG (Retrieval-Augmented Generation): A method where the AI first searches external data sources (like documents or databases) for relevant info before generating a response. We love a thoughtful queen who checks her sources and only hallucinates minimally.

Reinforcement Learning: Teaching AI through a system of rewards and penalties. The robot version of sticker charts and timeouts.

Transfer Learning: Adapting a model trained for one task to perform another. Like switching from Excel to Canva without a meltdown.

🧠 Inside the AI Brain: How It Thinks

Completion: The AI’s generated output in response to a prompt. What AI gives back.

Context Window: The amount of text or information an AI model can “remember” in a single interaction. TL;DR: It has memory issues.

Embedding: A way of turning words, sentences, or images into numbers so AI can compare them. Kind of like AI Tinder.

Inference: The process of running a trained model to get a prediction or output. Magic trick, but make it math.

Prompt: The question, instruction, or input you give to an AI system. What you type in.

Prompt Engineering: Crafting inputs to get more useful or accurate AI responses. The art of getting the AI to respond exactly how you want it.

Token: The basic units (words or parts of words) AI uses to read and generate text. Think of them as AI's building blocks.

🛠️ Under the Hood: Tools & Interfaces

API (Application Programming Interface): A bridge that lets different software talk to each other; often used to integrate AI into apps. Fancy tech way of letting apps collaborate.

Autonomous Agent: An AI system that can plan, execute, and adapt tasks over time with minimal user input. An AI that doesn’t need babysitting.

LangChain: A developer tool for connecting LLMs with data sources, APIs, and workflows. The glue that makes smart AI apps even smarter.

MCP (Model Context Protocol): The underlying system that enables AI models, particularly Large Language Models (LLMs), to interact with external tools, data sources, and services in a standardized and efficient manner. The behind-the-scenes traffic cop for AI.

Plugin: A small add-on that extends an AI model’s capabilities (e.g., access to live web results or booking tools). Adds extra skills to your AI, like turning it into a travel agent or fact-checker.

Vector Database: A special kind of database used to store embeddings for fast similarity searches. It’s how AI remembers the vibes. Forget what embeddings are? You’re not alone. See above.

🔧 For the Builders: Development & Deployment

Compute: The hardware power (CPUs, GPUs) needed to run and train AI models. The horsepower behind your AI.

Fine-tuning: Customizing a pre-trained AI model with new, task-specific data. Giving a pre-trained model your personal flair.

Latency: How fast a model responds. Speed queen.

Model Training: Teaching an AI system using data to recognize patterns and make predictions. The foundational grind where your model learns the ropes.

Sandbox Environment: A test area where developers experiment with models safely. Safe space for devs to play mad scientist.

Throughput: How many tasks a model can handle in a set amount of time. Capacity queen.

Zero-shot / Few-shot Learning: Having AI complete tasks with little to no examples. Doing a task with little to no context. Brave.

📊 How’s It Performing?: Metrics That Matter

A/B Testing: Running two versions of something to compare which performs better. Like sending two outfit options to the group chat and going with the one that gets more “🔥” texts.

Benchmark: A standard test used to compare the performance of different models. The AI Olympics.

Accuracy: How often an AI model makes the correct prediction. Did it get the answer right?

F1 Score: A balance between precision and recall. The Goldilocks of model metrics.

Precision & Recall: Measures of an AI model’s quality when identifying true vs. false outputs. Did it get the right stuff and not the wrong stuff?

🔒 Ethics Check: Building Better AI

Bias: When AI systems produce unfair or skewed results based on flawed data. The digital version of side-eyeing stereotypes.

Data Privacy: Protecting personal or sensitive data used in training or deployment. Keeping your info safe and sound.

Explainability: Understanding why AI gave a specific answer. We need to know why, not just what.

Fairness: Ensuring AI performs equitably across groups. Everyone deserves a fair shot, even in machine logic.

Responsible AI: Building and using AI in ways that are ethical, transparent, and safe. Like tech with a conscience.

Transparency: Making it clear how AI decisions are made. No black boxes, please.

Before You Go: Hey, don’t gatekeep this glossary. If this helped you feel even 10% more confident talking AI, chances are your group chat, team Slack, or favorite founder friend will thank you for the heads up. Share the knowledge, spread the clarity, and let’s all show up a little smarter together.

The future is female, fluent, and AI-literate, and you’re leading the way. ⤵️ 

P.S. Who’s behind this?
We’re Katie and Julie: two tech-obsessed founders, former corporate warriors, and current moms of littles who are very much in the juggle. Between us, we’ve led digital strategy for Fortune 500s, hit the Inc. 500 list, scaled and exited brands, and survived more than one toddler tantrum mid-Zoom. Now? We’re on a mission to help you cut through the AI noise, save time, make more money, and actually feel excited about the future.