Every definition is written at a sixth-grade reading level and still technically correct — with an analogy, a job-site example, and a business example for each term.
Word of the Day
“IN-fer-ens”
Inference is when an AI uses what it has already learned to answer a question, make a prediction, or complete a task.
Think of it like… Studying for a test is training. Taking the test is inference.
Why it's featured — Featured because inference is the moment AI actually does its job. Once you know this word, every conversation about speed, cost, and quality of AI makes more sense.
Terms are grouped into numbered lessons of ten. New lessons are added over time.
Lesson #1
10 terms · Nano AI-Driven Ecosystem, Agent, Multi-Agent System…
Lesson #2
10 terms · Prompt, Prompt Engineering, Training Data…
Lesson #3
10 terms · Machine Learning, Neural Network, Token…
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30 terms
“AY-jent”
An AI worker that has a specific job to do.
“AL-go-rith-um”
A list of steps that tells a computer how to solve a problem.
“A-P-I”
A doorway that lets two pieces of software talk to each other.
“aw-toh-MAY-shun”
Setting up work so it happens by itself, without someone doing it each time.
“aw-TON-oh-mus AY-jent”
An AI agent that can complete tasks on its own within rules you give it.
“BENCH-mark”
A standard test used to compare how well different AI models perform on the same task.
“kal-ih-BRAY-shun”
Adjusting an AI system so its answers become more accurate and reliable.
“KON-text WIN-doh”
How much information an AI can hold in mind at one time.
“em-BED-ing”
Turning words or documents into numbers so a computer can tell which ones mean similar things.
“FYNE TOON-ing”
Extra training that teaches a general AI the specifics of your work.
“GARD-rayls”
The rules and limits you set so an AI stays safe and on task.
“huh-LOO-sih-NAY-shun”
When an AI confidently gives an answer that is simply made up.
“HYOO-mun in the LOOP”
A setup where a person reviews or approves what the AI does before it counts as final.
“IN-fer-ens”
Inference is when an AI uses what it has already learned to answer a question, make a prediction, or complete a task.
“LAY-ten-see”
The delay between asking an AI something and getting the answer back.
“muh-SHEEN LURN-ing”
A way of teaching a computer by showing it lots of examples instead of writing out every rule.
“MOD-ul”
The AI's brain that has learned from lots of information.
“MUL-tee AY-jent SIS-tem”
Many AI agents working together while sharing information.
“muhl-tee-MOH-dul”
An AI that can handle more than one kind of input — text, images, audio, or video together.
“NAN-oh A-I EE-koh-sis-tem”
A group of very small AI programs that each have one job, but they work together like a team to solve bigger problems.
“NOOR-ul NET-wurk”
A math structure loosely modeled on the brain, made of layers that pass signals along to spot patterns.
“OR-kes-tray-tor”
The AI manager that tells all the other AI agents what to do.
“PROMPT”
The instructions or question you give an AI so it knows what you want.
“PROMPT en-jih-NEER-ing”
The skill of writing instructions that get better answers from AI.
“RAG”
A method where the AI looks up your real documents before answering, instead of guessing.
“TEM-pur-uh-chur”
A setting that controls how predictable or creative an AI's answers are.
“TOH-kun”
A small chunk of text — roughly part of a word — that an AI reads and writes one piece at a time.
“TRAY-ning DAY-ta”
The information an AI studied in order to learn.
“VEK-tor DAY-ta-base”
A special kind of memory that helps AI quickly find information that is similar.
“VEK-tor DAY-tuh-bays”
A place to store embeddings so an AI can quickly find the most similar pieces of your content.