AI Chat Answers That Invent Shipping Policies, and How to Audit Them
A support chatbot that states a delivery time the store never promised creates refunds, chargebacks, and screenshots. Here is an audit routine for the answers a chat tool gives on your policies.
The chat tool did not lie on purpose. It interpolated. Asked whether an order would arrive by Friday, it combined a dispatch estimate from one help page, a carrier range from another, and produced a confident date that no single source in the store had ever stated. The customer kept the screenshot. Support honoured the date with an upgrade, and the cost landed nowhere near the chat tool's budget line.
Policy answers are the highest risk class a storefront chat gives, because they create commitments: shipping times, return windows, warranty terms, price matching. Product chatter can be vague safely. A policy sentence will be quoted back at you, word for word.
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Build a question bank from real tickets
Pull the last few hundred support contacts about shipping, returns, and warranty, and turn the common questions into an audit set, including the awkward versions: a holiday deadline, an international order, a split shipment, a preorder. Audit with the questions customers ask, not the questions the help centre wishes they asked.
Run the set monthly and after every policy change, and save the answers with dates. The dangerous failure is not a wrong answer on day one, it is a correct answer in March still being given in June, a month after the return window changed and nobody updated the chat source or checked that it picked the change up.
Require a source for every policy claim
Configure and test for grounding: each policy answer should come from a named, current help page, and the tool should say it does not know rather than compose an answer when the source is silent. Test the silent cases specifically. Ask about a destination you do not ship to, a product category with no warranty, a service you do not offer. A tool that invents politely on those is not ready for the questions you do cover.
Where the tool supports it, have the answer link or name its source page. That helps the customer, and it helps you: when an answer is challenged, you can find in a minute whether the source page or the tool's reading of it was wrong, and fix the right one.
Put a price on wrong answers
Track contacts that cite the chat, refunds or upgrades given because a chat answer set an expectation, and the handling time they consume. Present that cost next to the tool's deflection rate. A deflection metric that counts a wrong answer as a saved ticket is the blended conversion rate problem all over again: the average hides the exact cases that cost money.
None of this argues for removing chat. It argues for scoping it: policies answered from sources with a refusal fallback, everything else flagged for human review sampling. The stores that get hurt are the ones that switched it on, watched deflection rise, and never read a transcript again.
Frequently asked questions
Why does a chat tool invent policy details?
Generative tools produce a plausible answer from patterns across their sources. Where sources are partial or conflicting, the tool fills gaps fluently instead of stopping. Grounding it in specific current pages, and allowing it to decline, reduces that behaviour but must be tested.
How often should chat policy answers be audited?
Monthly as routine, and immediately after any change to shipping, returns, or warranty policy. Most damaging errors are stale answers: correct when written, wrong after the policy moved, and still delivered with full confidence.
What questions belong in an audit set?
Real customer phrasing from support tickets, covering standard cases plus edges: holiday cutoffs, international delivery, split shipments, preorders, and services the store does not offer. The negative cases test whether the tool declines instead of inventing.
Should chat answers cite their source?
Where possible, yes. Naming or linking the policy page lets customers verify the answer and lets the store diagnose errors quickly, distinguishing a wrong source page from the tool misreading a correct one.