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Building and running AI

Terms for building and running AI

What happens behind an AI app: APIs, inference, GPUs, embeddings, vector databases, RAG, open-source models and running AI on your own device. 14 terms

All terms 162 AI basics 17 Generative AI and LLMs 22 Chatbots and assistants 14 Prompts and settings 16 Images, video, audio and writing 17 Agents and automation 13 Machine learning 28 Building and running AI 14 Ethics, safety and privacy 21

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14 terms

A

AI benchmarkAn AI benchmark is a standard test used to measure and compare how well AI models perform on a task, such as maths problems, coding or general knowledge.AI evalsAI evals are repeatable tests that check whether an AI feature gives good results, using set inputs and clear scoring rules.AI wrapperAn AI wrapper is an app built on top of someone else’s AI model, adding its own interface, prompts or workflow rather than its own model.APIAn API (application programming interface) lets one program use another’s features. AI companies offer APIs so developers can build their models into apps.

E

EmbeddingAn embedding is a list of numbers that represents the meaning of a piece of text, an image or another item, so a computer can measure how similar things are.

G

GPUA GPU (graphics processing unit) is a chip that runs many calculations at once. Built for graphics, GPUs are now the main hardware for training and running AI.

I

InferenceInference is when a trained AI model is used to produce an answer, such as a chatbot writing a reply, as opposed to the training stage when it learns.

L

Local AILocal AI means running an AI model on your own phone or computer instead of on a company’s servers, so it can work offline and keep your data on your device.

N

NPUAn NPU (neural processing unit) is a chip built to run AI tasks efficiently on phones and laptops, without draining the battery or sending data to the cloud.

O

Open-source AIOpen-source AI means models that are free to download, use and modify. Many are “open-weight”: the trained model is shared, but not the data it was trained on.

Q

QuantizationQuantization shrinks an AI model by storing its numbers with less precision, so it uses less memory and runs faster, with a small loss in quality.

R

Retrieval-augmented generation (RAG)Retrieval-augmented generation (RAG) makes AI answers more accurate by first looking up relevant information, then having the model answer using it.

S

Semantic searchSemantic search finds results based on what you mean rather than the exact words you type, using AI to understand the intent behind a query.

V

Vector databaseA vector database stores embeddings and quickly finds the ones most similar to a query, which lets AI apps search by meaning across large collections.

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