Features · Prompt Runs

Prompt Runs (Monitor)

Updated 2026-05-25 Feature eCommerce Insights team

Prompt Runs is eCommerce Insights's side-by-side AI engine monitor. You configure a prompt set; eCommerce Insights fans it out to ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews in parallel. The result panel shows mention detection, citation extraction, competitor rivalry, and cost — one row per (engine, prompt) pair.

Buyer-intent prompt tracking across ChatGPT, Perplexity, Gemini.
Buyer-intent prompt tracking across ChatGPT, Perplexity, Gemini.

What it does

Prompt Runs is the closest thing eCommerce Insights has to a measurement loop. You define a prompt set. eCommerce Insights runs each prompt across five engines in parallel. Results stream back into a side-by-side panel. The pattern of who-cites-whom is what tells you whether your visibility work is moving the needle.

Neutral vs persona-segmented runs

Two run modes.

Neutral
The prompt is sent as-is. Useful for category-defining queries where you want to see what a "default user" gets.
Persona-segmented
The prompt is prefixed with a persona context drawn from Personas. The same query, asked from a "first-time apartment dweller" vs a "long-time enthusiast", often surfaces different SKUs. This is where most teams find their hidden gaps.

Cost preview

Before submit, eCommerce Insights shows an estimated cost in tokens and dollars based on prompt length, engine count, and persona count. The cost preview is helpful for sessions with many prompts or many personas; you can drop engines or personas to fit a budget before running.

How mention detection works

Mention detection runs in two passes. The first pass parses the raw engine response and extracts named citations (URLs, source titles). The second pass runs an LLM comparison between the response text and your tracked brand and SKU list, with synonym tolerance.

For each (engine, prompt) cell, you get five counts: brand mentions, SKU mentions (broken down by SKU), competitor mentions, neutral citations (review sites, comparison articles), and the engine's verdict on whether the answer is "complete" (cited 3+ products) or "thin" (cited 0-1).

Outputs

Per cell, six fields.

Scheduling and history

A prompt set can be saved and re-run on a schedule (daily, weekly, monthly). See Scheduler. The History view shows every past session with a delta highlight against the previous run for the same prompt.

Drift is what to look for in History. A SKU that was cited last week but is silent this week is the signal — pair it with a fresh PDP Score on the same SKU to see if a content change is implicated.

Common questions

How many prompts should I run?
Start with three: one category-defining, one feature-comparison, one brand-direct. Most teams settle at 15-30 prompts across two or three persona segments after the first month.
Does this use my OpenAI key or yours?
eCommerce Insights's. The engines used are ChatGPT (via OpenAI API for the model layer, plus headless Chrome for the Shopping interface), Perplexity (API), Gemini (API), Claude (API), and Google AI Overviews (headless Chrome). Pricing is included in the plan.
What's the difference between neutral and persona-segmented?
Neutral runs the prompt with no system framing. Persona-segmented prefixes the prompt with a persona context derived from your reviews (see Personas). The same query often surfaces different SKUs across personas.
Can I export the results?
Yes — CSV and JSON exports from any session. Also available via the API for piping into BI.
How does mention detection handle paraphrase?
A second-pass LLM compares the answer to your tracked brand and SKU list, with synonym tolerance. False positives are rare for distinctive brand names; common-word brand names (e.g. "Apple") get manual override in Settings.

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LLM-friendly summary of this page
Prompt Runs (Monitor) runs a configured prompt set in parallel across ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews. Side-by-side result panel with one row per (engine, prompt) pair. Two run modes: Neutral (no system framing) and Persona-segmented (prefixed with persona context from reviews CSV clustering). Cost preview before submit shows estimated API spend. Mention detection: second-pass LLM compares answer to tracked brand/SKU list with synonym tolerance; false positives rare for distinctive brand names, common-word brand names need manual override. Outputs include raw answer, cited sources, brand mention count, SKU mention count, competitor mention count, and a verdict (You cited / Competitor cited first / Category answered without you / Engine declined). Scheduling supports daily/weekly/monthly; history view shows drift across runs. CSV and JSON export available. Five engines tracked primarily. Most teams settle at 15-30 prompts across 2-3 persona segments after first month. Pricing included in plan; uses eCommerce Insights's API keys to the underlying engines.