RAG means Retrieval-Augmented Generation. It's mostly used in casual online conversation. It's casual and generally safe with friends and online. It's standard online vocabulary; nothing inherently concerning about hearing it.
RAG
Retrieval-Augmented Generation.
Etymology timeline
Born ~2020 · no dated citations yetSeen in the wild
"Alphabet Inc.") stock value.[\[4\]](https://en.wikipedia.org/wiki/Retrieval-augmented_generation#cite_note-Ars_Technica-2024-4) RAG is used to prevent these errors, but it does not solve all the problems.
If an [internal link](https://en.wikipedia.org/w/index.php?title=Special:WhatLinksHere/Rag&namespace=0) incorrectly led you here, you may wish to change the link to point directly to the intended article.
Semantics, how and which exactly?<br>Believe me, RAG is the finest of technical masterpiece there is.
Definitions
Retrieval-Augmented Generation. A pattern where an LLM pulls relevant documents from a vector database before answering, grounding output in real information.
"We're building a RAG app so the bot can cite our actual product docs."
RAG means Retrieval-Augmented Generation. It's mostly used in casual online conversation. It's casual and generally safe with friends and online. It's standard online vocabulary; nothing inherently concerning about hearing it.
"RAG" is slang. It means: Retrieval-Augmented Generation. Register: casual. Use it in: casual conversation and online posts. Avoid in: formal writing and professional emails.
“"Alphabet Inc.") stock value.[\[4\]](https://en.wikipedia.org/wiki/Retrieval-augmented_generation#cite_note-Ars_Technica-2024-4) RAG is used to prevent these errors, but it does not solve all the problems.”
“If an [internal link](https://en.wikipedia.org/w/index.php?title=Special:WhatLinksHere/Rag&namespace=0) incorrectly led you here, you may wish to change the link to point directly to the intended article.”
“Semantics, how and which exactly?<br>Believe me, RAG is the finest of technical masterpiece there is.”
Comments
Retrieval-Augmented Generation — feeding an LLM live, retrieved-from-a-database context at inference time so it doesn't hallucinate. The architectural pattern that quietly powers most production AI products in 2026.
We're not training a model — we're just doing RAG over the help docs and letting GPT do the rest.
Comments
For specific readers
Why is this section here?
Slang is read by different audiences for very different reasons. The For parents view re-explains the term in plain, non-judgemental language so a caregiver can understand what their child means without needing the surrounding subculture. The For English learners view focuses on register (when it's safe to use), formal alternatives, and common mistakes — what a textbook won't teach you.
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Semantically related
- grounding80%
Tying an AI's output to verified external facts (documents, databases, search results) to reduce hallucination. RAG is the most common technique.
- fine-tuning65%
Continuing to train a foundation model on a smaller, specialized dataset so it specializes in your task. Increasingly replaced by RAG + prompt engineering for most use cases.
- prompt engineering63%
The craft of writing inputs to LLMs to reliably produce the output you want. Includes role-setting, few-shot examples, chain-of-thought, and careful phrasing.
- tool use63%
The ability of an LLM to call external functions (search, code execution, databases) mid-conversation. The building block of agentic systems.