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.
Transformer
The neural network architecture behind modern LLMs, using self-attention to process all tokens in parallel.
Attention Mechanism
A mechanism that lets a model weigh which parts of the input matter most for each output token.
Tokenizer
The component that splits raw text into tokens the model can process, and converts them back to text.
Diffusion Model
A generative model that creates images by gradually denoising random noise, powering tools like Stable Diffusion.
RLHF (Reinforcement Learning from Human Feedback)
A training technique that aligns models with human preferences using a reward model learned from human ratings.
Chain-of-Thought (CoT)
A prompting technique where the model reasons step by step, improving accuracy on complex tasks.
Few-Shot Learning
Guiding a model by providing a few examples in the prompt, without any additional training.
Temperature
A sampling parameter that controls randomness: lower values are more focused, higher values more creative.
Quantization
Reducing the numeric precision of model weights (e.g. to 4-bit) to shrink size and run models on less hardware.
GGUF
A file format for storing quantized models, widely used to run LLMs locally with tools like llama.cpp and Ollama.
Mixture of Experts (MoE)
An architecture that routes each token to a few specialized sub-networks, boosting capacity without proportional cost.
Function Calling / Tool Use
A capability that lets a model output structured calls to external functions or tools, enabling agents and integrations.
Knowledge Distillation
Training a smaller 'student' model to mimic a larger 'teacher' model, retaining most quality at lower cost.
AI Alignment
The field focused on ensuring AI systems behave in line with human values and intended goals.
Zero-Shot
Asking a model to perform a task with no examples, relying entirely on its pretrained knowledge.
Agentic AI
AI systems that pursue a goal autonomously — planning, using tools, and taking multi-step actions — rather than just responding to a single prompt.
Vibe Coding
A coding style where you describe what you want in natural language and let an AI generate and iterate on the code, focusing on intent and results over writing every line by hand.
Reasoning Model
An LLM trained to 'think' before answering — spending extra compute on internal step-by-step reasoning to solve harder math, coding, and logic problems.
Text-to-Video
AI models that generate video clips directly from a written description, controlling scene, motion, and style through the prompt.
Foundation Model
A large model trained on broad data that serves as a general-purpose base, adaptable to many downstream tasks via prompting or fine-tuning.
Guardrails
Safety controls around an AI system that constrain what it can say or do — filtering harmful content, limiting tool access, and enforcing policies.
System Prompt
The hidden instruction that sets an AI assistant's role, rules, and style before any user message — the foundation of its behavior.