AI Glossary

AI terms, in plain language

No jargon, no hype — just clear definitions of the AI terms that actually matter when you're running a business.

AI-native: Software where AI is the core operating layer — the thing that reads data, decides, and acts — rather than a feature bolted onto an otherwise ordinary app.
AI-native product studio: A company that builds software where AI is the operating layer and owns the products it ships, as opposed to an agency that configures other people's tools by the hour.
AI agent: Software that reads your data, decides what to do, and takes action across your tools — creating records, routing work, drafting responses. A chatbot only replies; an agent acts.
Agentic workflow: A multi-step process an AI agent runs across tools, taking actions in sequence and escalating to a human when judgment is needed or confidence is low.
Business operating system: One AI-native layer that unifies a company's CRM, messaging, and operations into a single system that runs the work itself, instead of a stack of disconnected tools.
Automation infrastructure: Event-driven flows that move work between your tools automatically — when something happens, the next step runs with no human in the loop.
Chatbot: Software that responds to prompts with text. Unlike an agent, it takes no action in your systems — a human still has to act on what it says.
LLM (Large Language Model): An AI model trained on vast amounts of text that can understand and generate language. LLMs are the engine behind most modern AI features.
RAG (Retrieval-Augmented Generation): A technique that grounds an LLM's answers in your own documents and data, so responses reflect your reality instead of only the model's training.
Human-in-the-loop: A design where a person reviews or approves an AI system's action at key points, keeping humans in control of high-stakes decisions.
Guardrails: The rules, approval gates, action logs, and confidence thresholds that keep an AI system's behaviour safe, predictable, and auditable.
Prompt: The instruction or input given to an AI model to produce a result.
Fine-tuning: Further training a general model on specific data to specialise its behaviour for a particular task or voice.
Embedding: A numeric representation of text that captures its meaning, letting software search and match content by concept rather than exact words.
Vector database: A store for embeddings that retrieves information by meaning — the memory that powers retrieval-augmented generation (RAG).
Workflow automation: Replacing manual, repetitive steps with automatic triggers and actions so routine work happens without a person shepherding it.
Orchestration: Coordinating multiple models, tools, and steps into one reliable process — the plumbing that makes an AI system dependable.
Evals: Tests that measure whether an AI system produces correct, reliable outputs, so you can trust it before and after it ships.
WhatsApp Business API: The official interface for businesses to send and receive WhatsApp messages at scale — the foundation our Yinpsi platform is built on.