AI Glossary
Plain-language explanations of key AI concepts, from LLM and RAG to fine-tuning and agents.
LLM (Large Language Model)
A neural network trained on massive text that predicts the next token, powering chat, writing, coding, and more.
Token
The basic unit of text a model processes; roughly 4 English characters or 1–2 Chinese characters per token. Billing and context limits are measured in tokens.
RAG (Retrieval-Augmented Generation)
A technique that retrieves relevant documents from a knowledge base and feeds them to the model so it answers grounded in your data.
Embedding
A numeric vector representation of text (or images) that captures semantic meaning, enabling similarity search.
Fine-Tuning
Further training a pre-trained model on your own data so it masters a specific style, format, or task.
LoRA (Low-Rank Adaptation)
A parameter-efficient fine-tuning method that freezes base weights and trains small low-rank matrices, drastically cutting cost.
Prompt Engineering
The craft of designing inputs (prompts) to reliably get high-quality outputs from a model.
AI Agent
An AI system that autonomously plans, uses tools, and takes multi-step actions to accomplish a goal.
MCP (Model Context Protocol)
An open standard that lets models connect to external tools and data sources in a unified way.
Context Window
The maximum number of tokens a model can consider at once; content beyond it is truncated or forgotten.
Hallucination
When a model confidently generates false or fabricated content. Mitigate with RAG, tool use, and verification.
Multimodal
Models that understand and generate across multiple data types, such as text, images, audio, and video.
Inference
The process of running a trained model to produce outputs, as opposed to training it.
Open Source / Open Weights
Models or tools whose source code or weights are publicly available to use, study, and self-host.
Vector Database
A database that stores embeddings and performs fast similarity search, the backbone of RAG systems.