AI terms, no jargon
Every term you'll actually meet in AI news, papers and product docs — explained in one line.
Agent
An AI that can plan, use tools and take multi-step actions on your behalf — not just answer questions.
Context window
How much text (in tokens) a model can consider at once. Bigger = can read longer documents.
Embedding
A numeric fingerprint of text/image so a computer can measure meaning and similarity.
Fine-tuning
Continuing to train a base model on your own data so it learns your style or domain.
Foundation model
A very large model trained on broad data, used as the base for many downstream tasks.
Hallucination
When an AI confidently makes something up. Fix with grounding (RAG) and citations.
Inference
The act of the model actually generating an answer for a request.
LLM
Large Language Model. An AI trained on huge amounts of text to predict the next token.
LoRA
Low-Rank Adaptation. A cheap way to fine-tune only a small slice of a big model.
MoE
Mixture of Experts. Only a subset of the model's weights fires per token — faster and cheaper.
Multimodal
A model that understands more than one type of input (text + image + audio + video).
Prompt
The instructions you give an AI. Better prompt = better output.
Prompt injection
An attack where hidden instructions in content trick an AI into ignoring its real orders.
Quantization
Shrinking a model by using lower-precision numbers so it runs on cheaper hardware.
RAG
Retrieval-Augmented Generation. Fetch relevant docs first, then let the LLM answer using them.
Reasoning model
An LLM trained to 'think step by step' internally before answering (e.g. o-series, R1).
RLHF
Reinforcement Learning from Human Feedback — how models are aligned to be helpful and safe.
Temperature
How random the AI's output is. 0 = deterministic, 1+ = creative.
Token
A chunk of text (~¾ of a word). Models read and generate one token at a time.
Vector database
A database that stores embeddings so you can search by meaning, not keywords.
Zero-shot
Asking a model to do a task with no examples — relying only on its general knowledge.