"If your articles are vague, Fin will be vague": the facts sheet and hard limits a store or SaaS needs before any AI answers customers

The fix for an AI that invents prices, discounts and policies is a short, owner-signed facts sheet plus hard limits. A longer prompt does not do it. The sheet states shipping, cutoffs, returns, plans and refunds in exact words. The limits say the AI grants no discount, refund or exception, and hands off anything the sheet does not cover. The stakes are on the record. In February 2024 a Canadian tribunal held Air Canada liable for its chatbot's wrong fare answer and ordered CAD $812.02⁵.

One tester's summary in r/Agent_AI (early 2026) gives this page its title: "If your articles are vague, Fin will be vague." Below are the cases, what they share, the sheet itself and a three-question test to run before go-live.

Why do AI support bots make up prices, policies and discounts?#

AI support bots make things up when the question falls in a gap between what the source says and what the bot is allowed to say. Five public cases show the pattern. Each one had a source problem or an authority problem, and usually both.

Sources as cited per row. The Shopify case is one owner's comment in a Reddit thread, not a verified incident.

The denominator is missing, and that matters. Nobody publishes how many bots answered a policy question correctly on the same days. These five are the cases that became public. They show the failure shape, not the failure rate.

Is the model the problem, or the source it reads?#

The common reading of these cases blames the model: it "hallucinated", so buy a better one or write a stricter prompt. The practitioners closest to the work point somewhere else.

• A founder of a human-powered support firm, on Hacker News in May 2026: "The tech isn't the hard part, the AI needs access to high quality context to answer questions accurately. But that requires more than just connecting your google drive or scraping your website."²
• The top answer in an r/CustomerSuccess thread (60+ comments, late 2025) on whether AI support is good now: "strongly depends on your knowledge base".
• An owner in r/AiForSmallBusiness (Feb 2026), after fixing a bot that gave wrong information: "the RAG + handoff flow matters way more than the fancy model."¹

A second explanation competes with the first, and it is harder to fix. A commenter on Hacker News in July 2026 wrote: "A sufficiently motivated attacker will be able to sweet talk the agent around whatever natural language guardrails were put in place."³ The Chevrolet case fits that description. A clearer source would not have stopped it. Removing the bot's authority to agree a price would have.

The BMW case adds a third variable. The root cause was a human employee's miscommunicated figure, which the bot then treated as the offer⁶. The model repeated a bad input faithfully. Three causes, then: a vague source, a persuadable bot with authority, and a wrong input. A facts sheet addresses the first and third. Hard limits address the second.

What does one wrong answer cost a store or a SaaS?#

The cost of a wrong AI answer has three parts: the promise you may have to honour, the order or subscription you may lose, and the fee you may still pay. Only the first has a court figure.

Honoured promises. Air Canada paid CAD $812.02 after the tribunal rejected its argument that the chatbot was "a separate legal entity"⁵. The BMW dealer paid roughly CAD $7,000 above its best offer⁶. These are large companies. We found no public case involving a small business. The principle in the Air Canada ruling does not depend on company size. This is not legal advice; check with counsel where you trade.

Lost orders. Illustration In Gorgias platform data (July 2026), AI-assisted conversations carried average order values of $190 after hours and $199 in hours¹³. Assume one wrong shipping or returns answer a week, and assume each one costs the order. That is 52 × about $195, or roughly $10,000 a year. Both assumptions are ours. Change them to your numbers.

The fee. Fin bills $0.99 per outcome, and its pricing page defines a resolution as "No further help is requested after Fin's last answer"⁸. A customer who reads a wrong answer and leaves without asking again meets that definition. Help Scout bills $0.75 and excludes conversations where the customer escalates, keeps asking or clicks "I still need help"⁹. Neither definition checks whether the answer was true.

Reviewers report the pattern in their own words. On G2 in 2026, Intercom reviewers wrote that "Fin will occasionally give answers that sound confident but aren't accurate" and "Sometimes the information Fin shares is incorrect, and the customer comes back upset"¹⁰. A July 2026 Trustpilot review of Gorgias said "the AI/bot has sent incorrect responses to customers, closed tickets without proper replies"¹¹. These are self-selected reviewers. They describe the failure. They cannot tell you how often it happens.

What goes on the facts sheet?#

A facts sheet for AI support is one page of exact statements the owner has checked and dated, plus a list of what the AI may and may not do. Each line answers one question with a number, a date or a condition. If a line needs "usually" or "it depends", it is not ready for the AI. Move it to the hand-off list.

Never-say list (venues and service businesses can copy this line by line):
- "We can probably do that for less."
- "That date is yours." (unless it is held in the calendar)
- "We'll waive the deposit / fee / minimum."
- "That's included." (unless the package list says so)
- Any price, discount or policy not on this sheet.

Two design choices carry most of the weight. First, Part C gives the AI no money authority, so a persuasive customer has nothing to talk it into. Second, the last row turns every gap in the sheet into a hand-off with a time, instead of a guess. Venues and other service businesses can use the same structure. Our wedding-vendor facts sheet shows a filled version for that trade.

The three-question go-live test#

The go-live test checks a facts sheet against real questions before any customer sees an AI reply. Run it on 20 past emails, chosen to include at least five about money or policy.

1. Is the answer on the sheet, word for word? For each email, find the exact line that answers it. If you cannot point to the line, the AI must hand off. Count the misses. Each one is a line to write or a topic to keep for yourself.
2. Is it about money or policy? Discounts, refunds, exceptions, deposits, delivery promises. If yes, the AI quotes the written policy only and routes any request beyond it to you. Try five persuasive versions ("my friend got 20% off", "your colleague promised") and confirm each one hands off.
3. Is the customer upset? A complaint goes to a person even when the sheet has the answer. The AI tells the customer when that person will reply.

Pass mark: zero answers that are not traceable to a line, and zero money concessions. Anything less, and the sheet is not ready.

What would change these conclusions?#

The evidence here has limits worth stating. The five cases are the public ones; their denominator is unknown. The Reddit material is snippet-grade, read from search results, so exact wording may differ from the full thread. G2 and Trustpilot reviewers choose to write, which skews toward bad experiences. The Gorgias order values come from its own platform, and it sells an AI agent.

Two findings would weaken the facts-sheet argument. One is a controlled comparison showing that bots fed full help centers make fewer policy errors than bots limited to a short sheet. The other is evidence that prompt-level guardrails reliably resist persuasion. We found neither. Owners appear to share the doubt: in Bluehost's 2026 survey of 350 US small businesses, 6% said they highly trust AI with their brand voice, and 32% named accuracy as their top fear¹².

A related question is who answers for the output. Intercom's AI terms, as quoted in a June 2026 Hacker News thread, say "Intercom makes no representations or warranties and provides no indemnities with respect to Output"¹⁴. We have not read the terms page directly. Read your own vendor's terms before go-live.

For the wider decision on whether to use AI at all, see should a startup use AI for customer support. For why customers write about their own order even when the FAQ is good, see why your FAQ page doesn't reduce support emails.

Where Valet fits#

Valet is built on the facts-sheet rule. It answers forwarded email, contact forms and listing leads only from the owner's own facts: price list, packages, calendar, policies and docs. It never invents a price, discount or policy, and anything not on the list waits for the owner. Each topic runs on Autopilot, Ask me first or Always you. Money, complaints, contracts and deposits go to the owner with one-tap replies, and the customer is told when the owner will reply. Every reply says it is AI and comes with a Valet receipt showing the question, the rule used and the sources used, with "Correct this" and "Pause topic". To see what your inbox looks like now, start with the free lead check: forward 10 to 20 recent inquiries and see what went unanswered.