Hallucination detection

How the check works

Engines summarize, condense, and sometimes invent. Detection runs at two layers: structured fields that can be diffed mechanically — price, SKU, variant labels, stock status — and free-text claims that need pattern matching against the PDP description, metafields, and supporting copy. The structured layer catches stale pricing and phantom availability; the free-text layer catches invented features and mixed-up variants.

Patterns vary by engine as of mid-2026: in eCommerce Insights's observations to date, shopping answers in ChatGPT lean toward feature invention (blending a product into a generic category answer), while Perplexity leans toward stale pricing from earlier indexing. Per-engine patterns matter because they imply different fixes — fresher structured data for staleness, more explicit attribute copy for invention.

Why it matters for ecommerce

A hallucinated price or feature is a conversion problem before it is a brand problem. A shopper arriving at the PDP expecting the AI-quoted price and finding a higher one often leaves; a shopper expecting a feature the product lacks refunds and reviews accordingly. Both costs land on the brand, not the engine.

There is a slower cost too: engines down-weight sources that produce correction-worthy answers, so persistent hallucinations erode the SKU's citation prospects over time. Detection and correction are AI-visibility maintenance, not a separate compliance chore. OpenAI's own documentation discusses why language models hallucinate; the merchant-side mitigation is making canonical facts trivially extractable.

A detection-and-fix loop: an example

A ceramic-mug brand sees ChatGPT describe its 12oz hand-thrown mug as "dishwasher and microwave safe" when the PDP says hand-wash only (illustrative example). The claim is free-text invention — the engine blended the mug into a generic ceramic answer. Detection flags the mismatch with the offending answer attached; the fix expresses care instructions as a typed metafield surfaced in structured data and adds an explicit care FAQ to the PDP. Subsequent refreshes show the engines converging on the correct claim — and the alert stays armed, because models change.

How it relates to neighboring terms

Hallucination detection is the accuracy check inside the broader monitoring stack: prompt tracking supplies the answers to check, AI sentiment analysis flags tone while this flags truth, and AI reputation management is the discipline that acts on confirmed errors. Strong Product schema is the best prevention — engines invent least where facts are explicit.

How eCommerce Insights does it

Every tracked answer is diffed against the connected catalog's canonical data — price, availability, variants, and typed attributes — and mismatches surface as alerts with the engine, prompt, and claim attached. Fixes ship as structured-data diffs, the form engines misread least.

Related terms

• AI reputation management — the discipline that acts on confirmed hallucinations.
• AI sentiment analysis — the tone check beside this truth check.
• Product schema — the strongest prevention layer.
• Prompt tracking — the answer stream detection reads.
• AI visibility — what persistent hallucinations slowly erode.