AI terms, in plain English
No jargon for jargon's sake. Clear, honest definitions of the AI concepts that actually matter when you're building a real product.
Agentic AI
Agentic AI is software that pursues a goal by reasoning, planning, and taking actions across tools and systems — not just answering a prompt.
ReadRetrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is a technique where an AI model retrieves relevant information from your own data and uses it to ground its answer — so responses are accurate and current instead of guessed.
ReadLarge Language Model (LLM)
A Large Language Model (LLM) is an AI model trained on vast amounts of text to understand and generate human language, powering tasks like writing, summarizing, extraction, and reasoning.
ReadMulti-Agent Orchestration
Multi-agent orchestration is coordinating several specialized AI agents — each handling part of a complex workflow — so they work together reliably toward one outcome.
ReadIntelligent Document Processing (IDP)
Intelligent Document Processing (IDP) is the use of AI to read, extract, verify, and route information from documents — like invoices, KYC forms, or bank statements — with little or no human effort.
ReadComputer Vision
Computer vision is AI that interprets images and video — detecting objects, defects, text, or patterns — turning camera feeds into structured, actionable data.
ReadMLOps
MLOps is the set of practices for deploying, monitoring, and maintaining machine-learning systems in production reliably — the discipline that keeps AI working after launch.
ReadFine-Tuning
Fine-tuning is further training a base AI model on your own domain data so it performs better on your specific tasks, tone, and terminology.
ReadAI Pipeline (Data Pipeline)
An AI (or data) pipeline is the automated flow that collects, cleans, and feeds data into an AI model and delivers its output to where work happens — the plumbing that makes a model useful.
ReadVector Database
A vector database stores data as numerical embeddings so an AI system can find information by meaning rather than exact keywords — the retrieval engine behind most RAG systems.
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