Most of the time, an AI that "hallucinates" isn't broken. It's just ungrounded: nothing stopped it from answering with something plausible instead of something true. Forrester's 2024 US Customer Experience Index found CX quality in the US fell to an all-time low for a third straight year, with underwhelming chatbot experiences named as one of the contributing factors. That's largely a grounding problem, not a model problem. Intercom's own Fin implementation guide says Intercom trained Fin to a 97% automation rate for informational questions once its content was properly maintained, a real number on what good grounding can do. Brainfish customer Vrio grounds its AI agent in its real API documentation and now resolves 80% of developer questions automatically, no engineer required.
How does grounding actually work?
At a basic level, grounding happens in two steps.
- Retrieval. Before the AI answers, the system searches a knowledge source for content relevant to the question: a help article, a past ticket, a product spec, a policy document.
- Generation. The AI then writes its answer using what it just retrieved, instead of relying only on what it learned during training.
This is why grounding is sometimes called "retrieval before generation." The model still writes the sentence, but it's writing from something in front of it rather than from memory alone.
Is grounding the same thing as RAG?
Mostly, yes. RAG, short for retrieval-augmented generation, is the specific technique most AI systems use to ground their answers. When people talk about "grounding an AI" in a support or product context, they're almost always describing a RAG setup: a retrieval step that pulls real content, followed by a generation step that answers from it.
Grounding is the goal. RAG is the method most teams use to get there.
What's the difference between grounding and fine-tuning?
They solve different problems, and teams mix them up often.
| Grounding (RAG) | Fine-tuning | |
|---|---|---|
| What it does | Retrieves current, specific content at answer time | Adjusts the model's underlying weights during training |
| How current the knowledge stays | As current as the source it retrieves from | Frozen at the point training happened |
| Best for | Facts that change: pricing, policies, product details | Tone, style, and how the model reasons |
| Traceability | Can point to the exact source used | Can't point to a specific source for an answer |
| Update speed | Update the source, the answer updates | Requires retraining the model |
Fine-tuning changes how a model talks. Grounding changes what it's allowed to say it knows. Most support use cases need grounding far more than they need fine-tuning, because the facts that matter (pricing, policy, product behavior) change constantly.
What happens when an AI's answer isn't grounded?
Without a source to retrieve from, the model still has to produce something, so it draws on the general patterns it learned during training. That's sometimes close enough for a general question, but rarely close enough for anything specific to a company's product, pricing, or policy.
Intercom's own implementation guide for Fin, referenced above, tells customers to run weekly content reviews and write for "clear structure, unambiguous phrasing" specifically because AI reads content differently than people do. That's a vendor, in its own documentation, describing what grounding actually requires: a knowledge source good enough to retrieve from in the first place.
Brainfish CEO Daniel Kimber has seen the same failure play out across support teams building their own AI agents:
"The most common failure mode is pointing an agent at stale, fragmented knowledge and then blaming 'hallucinations.' If the AI can't find the right source, it will fill the gap. That's not a prompt issue. The fix is governance: freshness, ownership, and observability into what was retrieved and what was ignored."
Daniel Kimber, CEO, Brainfish, in Brainfish's AI customer support guide
Why does grounding matter more in customer support than in casual AI use?
Ask ChatGPT a trivia question and a slightly wrong answer is a minor annoyance. Ask a support AI about your refund policy or a product's current pricing tier, and a slightly wrong answer becomes a support ticket, or worse, a customer decision made on bad information.
That's the gap between AI as a novelty and AI as something a business puts in front of paying customers. Brainfish is built around that gap specifically: it's a CX platform designed to be the most accurate answer engine for B2B support, grounding every response in a company's real docs, tickets, and transcripts, and running alongside the helpdesk a team already has, Zendesk, Intercom, Salesforce, rather than asking them to replace it.