## TL;DR
Bots invent policies because the prompt says 'be helpful' and the model fills gaps with plausible-sounding rules. The fix is grounding: every policy statement must cite a passage from your actual policy docs, and anything without a citation gets blocked or hedged. Uncited policy claims are the failure; the check is the cure.

## The query

```text
support bot stated a policy that doesn't exist: grounding check
```

## Use this when

- A support bot states policies that don't exist
- Customers quote the bot's invented policy back at your team
- You're designing grounding for a support agent


## Not for

- Bots giving wrong product facts (different grounding target)
- Tone or style problems
- Human agents misstating policy


## Steps

### 1. Collect the invented policies

Pull recent conversations where the bot stated a policy and check each claim against your real docs. Log the exact invented wording. You need real examples to test against, not hypotheticals.

Expected output: a list of invented policy statements with the bot's exact wording.

### 2. Build a policy source the bot must cite

Put your real policies in a retrievable store: refunds, shipping, warranties, account rules. The bot's instructions should require it to base policy answers on retrieved passages and to say 'I don't have a policy on that' otherwise.

Expected output: a policy corpus the bot retrieves from, wired into its instructions.

### 3. Add a citation check before sending

After the bot drafts a reply, verify that every policy claim traces to a retrieved passage. Claims without a source get rewritten as 'let me check with the team' instead of sent. This is the grounding check proper.

Expected output: no policy claim reaching a customer without a source passage.

### 4. Test with adversarial questions

Ask the bot about policies you don't have: 'what's your policy on X' for X that doesn't exist. The correct answer is always a hedge or a handoff, never an invented rule. Run this suite on every prompt change.

Expected output: a red-team suite the bot passes with hedges, not inventions.

## Variant phrasings

### ai support bot hallucinating policy

Steps 2 and 3: the retrievable corpus plus the citation check.

### chatbot making up refund policy

Step 4's adversarial suite catches the invention habit.

## Why it happens

Language models are trained to answer, and 'I don't know' is underrepresented in training. Faced with a policy question and no retrieved passage, the model does what it's rewarded for: produces a confident, plausible answer. Grounding works because it changes the task from 'answer' to 'answer from these passages', which the model can actually do.

## Edge cases

- Policy docs change. Version the corpus and re-test after every policy update.
- Hedging too much is its own failure. Tune so common policies answer crisply and edge cases hedge.
- The check needs the retrieved passages logged. Without logs you can't audit what grounded what.

## Provenance

Resolved from the public thread: https://vectle.com/posts/pst_YLcDMjFvMQoZGY1BihNeyQ