Prompts-GPT.com AI Visibility Self-Audit: Public Discovery, Evidence Exports, and Current Gaps

This review focuses on the public surfaces a buyer, crawler, or AI answer engine can see without login: homepage positioning, features, pricing, docs, resources, articles, the free checker, llms.txt, robots.txt, sitemap.xml, and share or export behavior.

The goal is not to prove that prompts-gpt.com already wins every answer. The goal is to prove that the product explains itself clearly, exposes enough evidence to be trusted, and avoids making claims that the public surface cannot support.

The free checker is useful for a first-pass visibility baseline because it shows answer snippets, detected sources, platform coverage, recommendations, opportunities, and downloadable markdown or JSON evidence. That is enough for a public diagnostic, especially when a buyer wants to test a domain before signing up.

The checker should not be treated as a permanent public report. Its rerun URL loads a live preview, so answers may change as providers change. For a stable artifact, prompts-gpt.com now publishes a dedicated self-audit markdown and JSON export alongside the methodology article.

The public discovery kit and llms.txt previously implied that the prompts-gpt.com self-check link was a reproducible public report. In practice it was a preloaded rerun state, which is weaker than a stable exported artifact or a documented self-audit page.

That mismatch matters because serious buyers compare AI visibility tools on proof surfaces, not only on feature claims. If the product says it supports exports, methodology, and transparent self-evaluation, those artifacts need a public home that explains the limits of the checker and the role of export files.

Public competitor research raises the minimum standard. OtterlyAI publicly documents prompt and citation CSV exports, a GEO audit PDF export, and a Looker Studio connector for shareable reporting. Peec documents both a documentation `llms.txt` index and a Looker Studio connector, while its source docs explain the difference between sources and citations plus gap analysis for competitor-owned mentions. Scrunch publicly documents citation-level grouping, topic filters, detailed drill-downs, and summary or detailed exports for sharing externally.

The lesson is straightforward: a credible AI visibility platform needs public discovery files, AI-readable docs, evidence export paths, and at least one transparent proof page that explains what its own outputs mean. Without that, the product starts to read like a generic dashboard with AI-themed copy.

Prompts-GPT.com can credibly claim prompt-level answer evidence, citations, competitor context, crawler-aware discovery guidance, public free tools, and exportable preview artifacts. Those are visible in the public product surface today.

It should avoid implying that llms.txt guarantees crawler usage, that a live rerun URL is a stable report, or that one preview score fully represents long-term answer visibility. Those stronger claims require recurring monitored data, not a single public check.

Use this page as the public explanation layer, then use the checker for live inspection and in-browser exports. When the domain is commercially important, move the work into a saved project so prompts, answer snapshots, competitors, and recurring reports can be tracked over time.

For prompts-gpt.com itself, the built-in project preset and monitor baseline are the right follow-up path: create or reuse the Prompts-GPT.com project, seed the baseline prompts, and treat the public self-audit as the outward-facing proof page while the monitor handles the ongoing measurement loop.