Compare context-first prompts. Measure recommendation shifts. Test with confidence.

Framing changes AI answers. A context-first conversation and a cold question can surface different brands, different recommendation criteria, and different cited sources, and Primed Prompts makes that difference measurable instead of anecdotal.

Buyers rarely ask one flat question. They describe a situation, then ask for a recommendation, and that earlier turn shapes what comes back.

• Brands that appear only once context has been set
• Brands that appear only in the cold, no-context path
• Differences in how the answer is structured and justified
• Differences in which domains are cited to support each answer

If your brand only appears in one of the two paths, that is a specific and fixable gap rather than a general visibility problem.

Every Primed Prompts run returns three aligned comparisons: which entities appeared in each path, how the response text differed, and which sources each path cited.

The entity table separates your brand from direct competitors and indirect publishers, so an answer dominated by review sites reads differently from one dominated by rivals.

• Total brands found, split into both-path, primed-only, and cold-only
• Per-domain rows marking whether you appeared in each path
• Full response text for every question in both paths
• The source list for each path, with citation-count deltas

The three views are read together, since a brand gained in one path usually traces back to a source the other path never cited.

Yes. Seerly runs the primed and cold paths on ChatGPT, Perplexity, and Google AI Overview, and how many of those engines are available to you is set by your plan.

Running the same pair on more than one engine is usually worth it, because engines weigh prior context and cite sources differently.

• Basic: one engine of your choice, either Google AI Overview or ChatGPT
• Pro: all three engines, ChatGPT, Perplexity, and Google AI Overview
• Enterprise: additional engines on request, under a custom scope
• Results reported per engine, never blended into one combined outcome

Only publicly available AI responses and public web pages are analyzed to build the comparison.

What a Primed Prompts run reveals about framing and citations feeds the rest of the workflow: which prompts you track, which sources you target, and which content gets written next.

The test is diagnostic, so its value comes from what it changes downstream rather than from the run itself.

• Tracked prompt sets, refined toward the framings buyers actually use
• Citation targets, when a path quotes a domain you have no presence on
• Content priorities, when the primed path raises criteria your pages never address
• Competitive views, when a rival holds only one of the two paths

Drafts produced off the back of a test still go through the approvals queue; nothing publishes on its own.

Primed Prompts is built to be rerun. The same two paths can be executed again after content or messaging changes, so you can check whether your inclusion and citation quality actually moved.

Because the prompt pair is fixed, a later run is a like-for-like comparison rather than a fresh, unrelated question.

• The same prompt pair rerun on a later date for direct comparison
• Entity counts compared between runs, primed against cold
• Source lists compared, to see whether new pages have been picked up
• Scheduled visibility runs alongside it, twice a week on Basic and more often on Pro

Seerly reports the measured before-and-after; it does not predict when a change will take hold.

Prompt tracking runs a fixed set of prompts on a schedule to measure visibility over time. Primed Prompts is a controlled experiment that runs one question two ways to isolate the effect of framing.

One is monitoring and the other is diagnosis, so they are usually used at different moments.

• Tracking: recurring runs, trends, rank movement, and competitor share
• Primed Prompts: a single question, two paths, one direct comparison
• Tracking answers whether your visibility is changing
• Primed Prompts answers why an answer included or excluded you

Most teams start from a tracked prompt where they underperform, then rebuild it as a primed and cold pair to find out what the answer is responding to.

No. Context changes which brands surface, but not always in your favor, and Seerly regularly shows brands that appear in the cold path and then drop out once criteria are set first.

That result is still useful. It usually means your pages match a broad category question but not the specific criteria buyers state.

• Primed-only brands, surfaced because the stated criteria matched them
• Cold-only brands, surfaced by general category association
• Both-path brands, the most durable position of the three
• The full response text for each, so the reasoning is visible rather than inferred

The comparison reports what happened on that run. It is not a promise about how a future run will resolve.

Primed Prompts works best on questions your buyers genuinely ask: a criteria or situation question to set context, followed by the recommendation question you actually want to win.

The pair has to be realistic. A context question written to flatter your brand produces a result you cannot act on.

• Start from tracked prompts where your visibility is weakest
• Use the objections and comparisons your sales team hears repeatedly
• Keep the cold question identical in intent to the primed product question
• Rerun after publishing to see whether the gap closed

Keyword and citation data are the usual starting point, since they show which terms and sources the answers are already built on.