The AI Glossary: sixty terms, honestly defined

Two to three sentences each, written for a person rather than a search engine — though the search engines are welcome. Terms are defined as used in mid-2026; the page carries a service date and will be re-serviced as the language moves.

A

Agent

A model given tools, memory, and a goal, allowed to take multiple steps on its own. The word covers everything from a script that retries twice to software that plans a workday.

Agentic workflow

A pipeline where the model decides the next step — searching, calling tools, revising — instead of a human sequencing every action.

Alignment

The training and tuning work that makes a model behave according to human intent and values: helpful, honest, refusing harm. The finishing pass after raw capability.

API

The programmatic doorway to a model: send a request with your prompt and settings, receive the model output. How software talks to AI without a chat window.

Attention

The transformer mechanism that lets every token weigh its relevance to every other token. The reason context matters at all — and the load-bearing idea of modern AI.

B

Batch processing

Running many requests together, typically slower but cheaper. The economical lane for jobs that do not need an instant answer.

Benchmark

A standardized test set used to compare models. Useful directionally; notoriously gameable; never a substitute for testing on your own cases.

C

Chain of thought

A model working through intermediate reasoning before answering. On newer reasoning-trained models this happens internally — asking for it explicitly is often redundant.

Checkpoint

A saved snapshot of a model at a point in training. What you actually download when you download a model.

Context engineering

The successor skill to prompt engineering: assembling everything a model sees — instructions, documents, examples, tools, memory — as one designed working set.

Context window

The maximum amount of text, in tokens, a model can consider at once. Grew from thousands to hundreds of thousands; ordering within it now matters more than fitting.

D

Deepfake

Synthetic audio, image, or video convincingly depicting a real person doing something they did not do. The reason provenance tools exist.

Diffusion model

The architecture behind most image and video generation: it learns to turn noise into pictures step by step, steered by your description.

Distillation

Training a smaller model to imitate a larger one — cheaper and faster to run, at some cost in capability. How much of the on-device world gets made.

E

Embedding

A list of numbers representing meaning. Texts with similar meaning get nearby numbers, which is what makes semantic search and RAG possible.

Eval

A repeatable test of model output quality against your own cases and rubric. The difference between it-seems-fine and knowing.

F

Fine-tuning

Continuing a model training on your own examples so it adopts a style, format, or domain. Heavier than prompting; lighter than pretraining.

Frontier model

The most capable model class available at a given moment, usually cloud-first. The moving line everything else is measured against.

Function calling

A model responding with a structured request to run a tool — search, database, calculator — instead of prose. The joint that makes agents possible.

G

GPU

The parallel processor that trains and often runs models. Its memory, VRAM, is usually the binding constraint for running models locally.

Grounding

Tying a model answer to supplied or retrieved sources instead of its trained memory. The main defense against fabricated detail.

Guardrails

The layer of rules, filters, and checks around a model that constrain what it will produce, separate from the model own training.

H

Hallucination

Confident, fluent output that is factually wrong or invented. Not lying — the model has no concept of truth, only likelihood. Verify accordingly.

Human-in-the-loop

A workflow with a mandatory human checkpoint before output ships. What separates using these tools from adding to the flood.

I

Inference

Running a trained model to get output — as opposed to training it. What you pay for per token; where the datacenter electricity goes after launch.

J

Jailbreak

A prompt crafted to trick a model past its safety training. An arms race this museum documents rather than participates in.

L

Latency

Time until the model starts and finishes responding. Network adds a few hundred milliseconds to cloud calls; on-device eliminates the round trip.

Local LLM / on-device AI

A model running entirely on your own hardware: private by construction, free per token, capped by your memory and the model size.

LoRA

Low-Rank Adaptation: small trainable add-on weights that customize a model cheaply — the standard way styles and subjects are added to open image models.

M

Mixture of Experts (MoE)

An architecture that activates only relevant portions of a large model per request — big-model capability at smaller running cost. Why capable local models became feasible.

Model collapse

Degradation that occurs when models train on machine-made copies of machine output. The Quarantine wing, stated as an equation: what trains on copies, dies of them.

Multimodal

A model that takes or produces more than text: images, audio, video, documents. Standard equipment on frontier systems now.

N

NPU

Neural processing unit — the accelerator in modern phones and laptops built specifically to run models efficiently on-device.

O

Open weights

A model whose trained parameters are downloadable. Licenses vary from fully permissive to research-only; the license, not the adjective open, is what matters.

Orchestrator

The controlling layer — human or model — that breaks a goal into tasks, assigns them, and reviews results. How large AI work is actually managed.

P

Pretraining

The long, expensive first phase: the model learns language and world patterns from a vast corpus. The pour, in this museum architecture.

Prompt injection

Malicious instructions hidden inside content a model reads — a webpage, an email — attempting to hijack its behavior. The classic attack on agents.

Provenance / C2PA

Cryptographic metadata attached to media recording what made it and how it was edited. The emerging infrastructure of is-this-real.

Q

Quantization

Compressing model weights to lower precision so they fit in less memory and run faster, at a small quality cost. The magic that puts big models on laptops.

R

RAG

Retrieval-Augmented Generation: search first, then have the model answer from what was found. The standard pattern for grounding answers in your documents.

Rate limit

The provider-imposed cap on how fast you can call an API. The reason production systems queue, retry, and batch.

Reasoning model

A model trained to deliberate internally before answering — slower, costlier, stronger on hard problems. Brief it with goals and budgets, not step-by-step scripts.

Red-teaming

Systematically attacking your own model or app — jailbreaks, injections, edge cases — before someone else does.

RLHF

Reinforcement Learning from Human Feedback: humans rate outputs, and the model is tuned toward what rated well. A core alignment technique; manners, made load-bearing.

S

Scaling laws

The empirical finding that capability rises predictably with more data, parameters, and compute. The economic engine of the entire buildout.

Schema / structured output

Asking a model for machine-readable output in an exact shape, usually JSON, and validating it. In 2026 the output contract is part of the prompt.

Semantic search

Search by meaning, via embeddings, rather than exact words. Finds renal failure when you searched kidney problems.

Slop

Low-effort machine-generated content published at volume for clicks and ad revenue. This museum antagonist — distinct from AI itself, which is the tool.

Streaming

Receiving a model answer token-by-token as it generates, rather than waiting for completion. Why chat interfaces feel alive.

Synthetic data

Machine-generated training data. Useful deliberately and in moderation; catastrophic as an accidental diet — see model collapse.

System prompt

The standing instructions a model receives before any user message: role, rules, boundaries. Where durable behavior lives.

T

Temperature

The randomness dial. Low means consistent and repetitive; high means varied and risky. Set it per task, not per superstition.

Token

The unit models actually read — roughly three-quarters of an English word. Context windows, prices, and speeds are all measured in these.

Training run

The weeks-to-months campaign of pretraining a model across thousands of accelerators. The single most expensive object in modern software.

Transformer

The 2017 attention-based architecture underlying essentially all modern language models and much else. The frame every floor is bolted to.

V

Vector database

A database built to store embeddings and find nearest-meaning matches fast. The retrieval half of RAG at scale.

VRAM

The GPU own memory. The hard wall that decides which models your hardware can hold; quantization exists to negotiate with it.

W

Watermarking

Embedding a detectable signature in generated output to mark it as synthetic. Technically fragile, politically inevitable, actively evolving.

Weights

The billions of learned numbers that ARE the model. Everything else — apps, chat windows, APIs — is furniture around them.

Z

Zero-shot / few-shot

Asking with no examples versus a handful. Examples remain one of the highest-leverage prompt moves per token spent.

Part of the Stay Human record. Practice the vocabulary in the syllabus; watch it get poured into being at The Assembly.