Grounding AI on your own data (RAG), explained simply
· Ian Bensley
Why AI grounded in your own content is accurate and on-brand while generic AI makes things up - and how retrieval-augmented generation actually works.
The single biggest difference between an AI tool that helps and one that embarrasses you is whether it's grounded in your own data. Here's what that means, without the jargon.
The problem with ungrounded AI
A raw language model answers from general training data. Ask it about your prices, your policies or your products and it may confidently make something up. That's fine for brainstorming, dangerous for customers.
What grounding does
Grounding (often called retrieval-augmented generation, or RAG) means the AI looks up the relevant facts from your documents and data first, then answers based on those. So it quotes your actual return policy, not a plausible-sounding invention.
Why it matters
Grounded AI is accurate, on-brand and trustworthy enough to put in front of customers or staff. It's the foundation of every AI agent I build - and, done right, it keeps your data in your control.
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