A generic AI model answers with what it learned during training -- not with your actual pricing, procedures, or today's catalogue. Left on its own, it can produce a plausible-sounding but wrong answer: what's known as a “hallucination.”
The problem with generative AI on its own
How RAG fixes that
Before answering, the assistant first searches your own documents (FAQ, pricing, procedures, catalogue) for the passage most relevant to the question, then writes its answer from that specific passage. That's the difference between guessing and checking.
In practice with CX-Flux: you upload your documents once, the assistant indexes them, and every answer is grounded in that base -- which you can update any time.
What that changes for your customer service
Answers that match your real pricing and procedures, fewer corrections after the fact, and an immediate handoff to you the moment a question falls outside what your documents cover -- instead of a made-up answer.
FAQ
Frequently asked questions
What is RAG, in one sentence?
A method that has the AI look up the right information in your documents before answering, instead of guessing or making one up.
Can the assistant make up a wrong answer?
RAG greatly reduces that risk by grounding answers in your real documents. For anything outside what it knows, the assistant hands the conversation to you instead of guessing.