AI & Data Glossary — Clear Definitions
A plain-English glossary of AI, machine learning, and data terms — RAG, MLOps, embeddings, vector databases, LLMs, fine-tuning, AI agents, and more.
Summary
Plain-English definitions of the AI, machine learning, and data terms that come up most in real projects — written to be clear and citable.
Clear, jargon-free definitions of the terms we use with clients every day. Each entry explains what it means and why it matters.
Embeddings
Embeddings are numerical vector representations of text, images, or other data that capture meaning, so similar items sit close...
Fine-Tuning
Fine-tuning is the process of further training a pre-trained AI model on a specific dataset to adapt its behavior — teaching it...
Generative AI
Generative AI is a category of artificial intelligence that creates new content — text, images, code, audio — by learning patte...
MLOps
MLOps (machine learning operations) is the practice of deploying, monitoring, and continuously maintaining machine learning mod...
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) is a technique that retrieves relevant information from a knowledge base and supplies it t...
Data Lakehouse
A data lakehouse is a data architecture that combines the low-cost, flexible storage of a data lake with the performance, struc...
Large Language Model (LLM)
A large language model (LLM) is an AI model trained on vast amounts of text to understand and generate human language — the tec...
Vector Database
A vector database stores and searches embeddings — high-dimensional vectors — to find the most semantically similar items quick...
AI Agent
An AI agent is a system that uses a language model to reason about a goal and take actions — calling tools, querying data, and ...
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