Grounding truth: RAG vs no RAG
Without RAG, it answers from memory
A language model replies from patterns it absorbed in training. It’s fast and fluent, but it can’t look anything up and it has never read your documents. When it doesn’t know, it may guess, with total confidence.
- What you get:
- a confident answer that nobody has checked.
- Try it:
- the example shop is made up, so you can spot the wrong answers.
With RAG, it answers from your sources
RAG stands for retrieval-augmented generation. Before answering, the system searches your knowledge base, pulls out the right passage and hands it to the model. That’s grounding: every answer tied to a source you can check.
- What you get:
- a current, checkable answer with its source.
- Still needed:
- good documents and good search. RAG reduces guessing, it doesn’t replace checking.
AI Partner PTY LTD, South Africa. Agents, fleets, orchestration, bespoke integration, AI services, software and app development, from idea to market.
Chat to Wes on WhatsApp or [email protected].