How to Keep a Marketing Agent's Product and Pricing Claims Accurate
Marketing AI agent accuracy on product details takes more than retrieval augmented generation. Use a claims register, price lookups and a check for each claim type.
On this page
- A wrong price belongs to the brand, whichever tool wrote it
- Name the record before you design retrieval
- What the prompt and the retrieval sources each have to do
- What structured outputs and tool calls can promise
- One verification pass, with a different check for each claim type
- Keeping the guardrails honest in production
- Sources
Marketing AI agent accuracy on product details owes less to retrieval augmented generation than the usual best practices suggest. Retrieval puts a passage in front of the model. It cannot promise the model restates a price exactly, and it has no view on whether that passage was approved, current or written for this market. Our position: a marketing agent built on agentic AI should never compose a price, a plan limit or a performance claim. It should look the number up, pick the claim from a register somebody owns, and pass a check suited to the claim type before a customer sees the copy.
A wrong price belongs to the brand, whichever tool wrote it
The FTC's advertising guide for small business (April 2001, edited January 2025) says "Advertisers must have evidence to back up their claims" and "The law requires that advertisers have proof before the ad runs." Its examples of material claims include a product's "performance, features, safety, price, or effectiveness".
A Canadian tribunal has held a company to its automated channel's words under a different body of law, negligent misrepresentation. In Moffatt v. Air Canada (2024 BCCRT 149, issued February 14, 2024), British Columbia's Civil Resolution Tribunal dealt with a website chatbot that suggested a customer could apply for bereavement fares retroactively, while the airline's own bereavement page said the policy did not apply to requests made after travel was completed. The decision reads: "It makes no difference whether the information comes from a static page or a chatbot." It found that Air Canada "did not take reasonable care to ensure its chatbot was accurate" and awarded $650.88 in damages. It is a small-claims decision from one province, and what carries over is its picture of the failure: two copies of one policy that disagreed, and a customer who acted on the wrong one.
Name the record before you design retrieval
Grounding AI agents in product details starts before retrieval augmented generation, and the best practice that matters most is naming one system of record for each type of claim. Prices live in the billing or pricing table, plan limits and feature availability in the plan matrix or product catalog, policies in the published terms. Blog posts, old decks and the marketing site are copies of those records, and copies drift, so we keep them out of the index for these claim types.
Marketing claims have no natural system of record, which is why they need a claims register: one row per approved claim, and the agent may use only claims that have a row. The rows below are a hypothetical example for an invented helpdesk product, and every value is illustrative. The evidence column exists because of the FTC's proof requirement.
| ID | Claim type | Approved wording or value | System of record | Owner | Evidence on file | Expires |
|---|---|---|---|---|---|---|
| P-01 | Price | Team plan, $49 per seat per month, billed annually | Billing price table | Pricing lead | Price table entry | When the record changes |
| F-07 | Feature availability | Single sign-on included on Business and Enterprise | Plan matrix | Product marketing | Release note | Next plan change |
| C-03 | Performance claim | One approved sentence about faster first responses | Claims register | Product marketing and legal | Study data, dated before first use | 12 months |
| L-04 | Policy | 30-day refund window, quoted from the terms | Published terms | Legal | Terms version | Next terms update |
What the prompt and the retrieval sources each have to do
An AI marketing agent's prompts and retrieval sources jointly decide whether its product details come out accurate, and vendor documentation is specific about both.
On the prompt side, Anthropic lists techniques that fit marketing copy: give the model permission to say "I don't know", have it extract word-for-word quotes before working from long documents, and have it find a supporting quote for each claim after drafting, under the rule that "If it can't find a quote, it must retract the claim." The same page states the limit: "while these techniques significantly reduce hallucinations, they don't eliminate them entirely."
On the source side, Anthropic's Citations documentation describes a feature, listed as generally available on the Claude API, that returns the exact passages supporting each claim. Plain text and PDF documents are chunked into sentences, while custom content documents use your blocks as supplied, which suits a register where each row is one citable unit. Google Cloud gives parallel advice for its grounding checker: it "recommends against supplying one very large fact that contains all of the information" and suggests smaller facts with attributes such as title, author or URL.
Our own proposal goes beyond those pages: attach a claim ID, owner, market and effective date to every chunk and filter on those fields before similarity ranking, so a retired price never becomes a candidate.
What structured outputs and tool calls can promise
LLM hallucination prevention for customer-facing claims and pricing works best when retrieval grounding, structured outputs, tool calling and evaluation each do one job, and the best practices follow from what each can promise.
Tool calling handles anything with a single right answer. Price, plan limit, stock and dates come back from a function that queries the record, and code inserts the value, so the model never types the digits. OpenAI describes a feature that "ensures the model will always generate responses that adhere to your supplied JSON Schema", and the same guide says "Structured Outputs can still contain mistakes." A schema cannot make a value true. What it can do is force every factual sentence into a field that carries a claim ID or source ID, which gives code something to verify.
One documented constraint shapes the build on Anthropic's API: "Citations cannot be used together with structured outputs", and a request that enables both returns a 400 error. We plan for two calls, a cited drafting pass and then a structured extraction pass.
One verification pass, with a different check for each claim type
LLM hallucination in retrieval augmented generation is caught by factuality verification after the draft exists, and for customer-facing claims and pricing the guardrails should differ by claim type. The table is our proposed implementation.
| Claim type | How the agent gets it | Automated check | Human approval |
|---|---|---|---|
| Price, discount, plan limit | Tool call to the record, value inserted by code | Every figure in the copy equals the looked-up value exactly | Pricing owner, when the check fails |
| Feature, spec, compatibility | Retrieval from the plan matrix or catalog, with citations | Sentence-level grounding score against the retrieved rows; low scores block | Product marketing reviews blocked sentences |
| Performance, outcome or comparative | Selected from the register by ID | Wording matches the row, evidence predates use, row has not expired | Legal or compliance for every new row |
| Policy, guarantee, eligibility | Quoted from the published terms | Quote matches the source text exactly | Policy owner on any change |
Two vendors document a grounding score that fits the feature row. Google Cloud's check grounding API "returns an overall support score of 0 to 1", adds citations for each claim, treats a sentence as a single claim, counts a partly supported sentence as ungrounded, and can return a score for each claim when claim-level scoring is enabled. Because it accepts "a human-generated blurb or a machine-generated response", the check can run again after an editor touches the copy. AWS documents a contextual grounding check in Amazon Bedrock Guardrails that scores each model response against a grounding source and a user query, with grounding and relevance thresholds configurable between 0 and 0.99. It lists summarization, paraphrasing and question answering as supported, and states: "Conversational QA / Chatbot use cases are not supported."
As documented, both tools score agreement with the sources you supply. Choosing sources that are approved and current remains your job, which is why the record and the register come first. For numbers we skip the threshold and compare values in code: a price either equals the record or the copy is blocked.
Keeping the guardrails honest in production
Grounding LLM outputs with retrieval augmented generation is meant to raise factual accuracy, and guardrails in production are how you find out whether it did. NIST's Generative AI Profile (AI 600-1, July 2024) suggests assessing output accuracy "by comparing it to a set of known ground truth data", and lists as a suggested action: "Review and verify sources and citations in GAI system outputs during pre-deployment risk measurement and ongoing monitoring activities." For a marketing agent that becomes a measurement plan:
- Before launch: a fixed set of briefs with known correct prices and claims, rerun whenever the prompt, model or register changes.
- Every run: the share of factual sentences carrying a claim ID or citation, blocked sentences by claim type, and exact-match failures on numbers.
- After publishing: corrections issued, credits or refunds traced to wrong copy, and the hours from a record change to the last live asset being updated.
The failure modes are predictable: an index that goes stale after a price change, two sources that disagree, an editor who improves an approved claim after the check has run, a register row that outlives its evidence. Each needs an owner in your governance operating model, and a separate verifying agent needs the handoff contract we describe for the multi-agent governance layer.
Verification adds tokens and reviewer minutes, so it belongs in your cost per run, and the corrections it prevents belong on the benefit side of an AI ROI calculation. Our agentic AI automation service can help design the register and the checks.
If you build only one piece of this, make it the price lookup with its exact comparison. It needs no model and runs in code on every draft. Then build the register, because retrieval, citations and grounding scores can only show that copy agrees with a source, and somebody still has to decide which sources the company stands behind.
Sources
- FTC: Advertising FAQ's: A Guide for Small Business (April 2001, edited January 2025)
- Civil Resolution Tribunal: Moffatt v. Air Canada, 2024 BCCRT 149 (February 14, 2024)
- Anthropic: Reduce hallucinations, Claude documentation
- Anthropic's Citations documentation: Citations
- OpenAI: Structured model outputs guide
- Google Cloud: Check grounding with RAG (last updated October 5, 2026)
- AWS: Use contextual grounding check to filter hallucinations in responses, Amazon Bedrock User Guide
- NIST: AI 600-1, Generative Artificial Intelligence Profile (July 2024)