Limitless AI
Best for professionals wanting organized conversation records
Limitless AI is a personal AI assistant that remembers conversations, meetings, and important information throughout your day.
81
Review AI Glossary · Last reviewed August 2026
RAG
· Retrieval-Augmented Generation
Hand-written by a real person. Reviewed against current practice in August 2026.
"
Definition
A method that fetches relevant documents at query time and feeds them to the model, so answers are grounded in real sources.
Why it matters
RAG solves one of AI's biggest problems: outdated or missing knowledge. Instead of relying only on training data, RAG lets a model search your documents, databases, or the web in real time and use that information to answer questions.
Tools that use RAG give more accurate, up-to-date answers with source citations. This is especially valuable for enterprise use cases where the AI needs access to internal company data.
How it works
4 stepsSTEP 01
Index your documents
Break your source material into chunks, convert each chunk into an embedding, and store it in a vector database.
STEP 02
Embed the question
When a user asks something, convert their question into the same kind of embedding using the same model.
STEP 03
Retrieve top matches
The vector database returns the chunks whose embeddings are closest to the question. Usually three to ten passages.
STEP 04
Generate the answer
The LLM receives the question plus the retrieved passages as context, and writes an answer grounded in what was retrieved.
Related terms
From the glossaryAI tools that use RAG
Hand-reviewed
SmartWriter AI
Best for content creators and marketers
AI writing assistant for blogs, emails, ads, and social media content.
79
Review Frequently asked questions
What is the difference between RAG and fine-tuning?+
RAG retrieves fresh external data at query time, so it stays current without retraining. Fine-tuning bakes knowledge into the model weights permanently but goes stale as the world changes.
Does RAG work with any LLM?+
Yes. RAG is an architectural pattern, not a model-specific feature. You can pair it with GPT-4, Claude, Llama, or any model that accepts retrieved context in the prompt.
Is RAG expensive to run?+
The main costs are the vector database and the extra tokens from retrieved chunks added to each prompt. For most production use-cases this is far cheaper than fine-tuning.
Browse related categories
New to RAG?
See the tools that use it.
The fastest way to understand RAG is to see it inside real products. Browse hand-reviewed tools that put it to work, each one checked by a person before it was listed.
Browse hand-reviewed AI tools