lAItest / AI field guide · edition 2026-09-26

Know the language. Build with clarity.

Plain-English definitions for the concepts, patterns, protocols, and tools behind modern AI work.

414 defined terms · 16 topic areas · 50 essentials

Generative & physical AI terms

8 matching terms.

Diffusion model Concept · Generative & physical AI
A generative model trained to reverse a progressive noising process.
Embodied AI Concept · Generative & physical AI
AI that perceives and acts through an agent situated in an environment.
Flow matching Concept · Generative & physical AI
Train a vector field describing a transformation between probability distributions.
JEPA Concept · Generative & physical AI
Joint Embedding Predictive Architecture: predict target representations within a learned embedding space.
Latent diffusion Concept · Generative & physical AI
Perform diffusion-based generation in a compressed representation rather than directly in the original data space.
Physical AI Concept · Generative & physical AI
AI interacting with physical systems, often through sensors, actuators, simulation, and learned policies.
VLA Concept · Generative & physical AI
Vision-language-action model: map visual observations and language to actions.
World model Concept · Generative & physical AI
A learned representation or predictive model of an environment and its dynamics.

Agents & architecture 23 — Who decides, what executes, and where control lives.
Goals, specs & plans 23 — Describe the outcome before delegating the implementation.
Loops, critics & adversaries 33 — Understand how work is challenged, repaired, and stopped.
Multi-agent coordination 21 — Delegation, ownership, context boundaries, and aggregation.
Context & memory 29 — What the model sees now, and what persists for later.
Tools & output contracts 20 — Model proposals become validated, authorized operations.
Protocols & interoperability 19 — Name the boundary: tools, agents, editors, or interfaces.
Coding-agent internals 25 — Instructions, skills, hooks, tools, and durable artifacts.
Retrieval & knowledge 31 — Find evidence, rank it, and preserve source boundaries.
Evals & observability 29 — Measure outcomes and inspect the execution path.
Inference & performance 28 — Latency, throughput, compute, and memory are different constraints.
Models, reasoning & training 36 — Separate weight changes from context and inference-time work.
Security & reliability 27 — Make privileges explicit and side effects recoverable.
Generative UI & voice 18 — The interaction layer has its own contracts and timing.
Generative & physical AI 8 — Broader model families beyond text-based assistants.
Tools & ecosystem 44 — Recognize the role before choosing the dependency.