Prompt Research
Make the actual usage purposes of LLMs visible. Prompt Research reveals prompt patterns through scientific reproduction and semantic reconstruction: cross-model consistent, privacy-compliant, and exclusively licensed through Rankscale. We estimate prompt volume honestly, not with an unverifiable panel count.
Trusted by 1000+ active users from brands and agencies worldwide
Why trust Prompt Decoding?
Exclusively licensed through Rankscale, Prompt Decoding is the only method that has been scientifically reproduced, cross-model validated, and recognized by the professional community.
Scientific reproducibility
Results independently validated in the OpenAI/Harvard study (NBER Working Paper 34255). Usage purposes and clusters were partially word-identical to Prompt Decoding findings published months earlier.
Cross-model consistency
Applied to both ChatGPT and Gemini, producing very similar clusters across models. Your strategy is not tied to a single vendor.
Community recognition
Presented at the G50 Summit 2025 by Hanns Kronenberg and won 3rd place at the SEO World Championship. Validated by the global SEO community.
Methodological basis
Relies on internal model simulations - revealing realistic prompt data from millions of real queries. Reproducible, privacy-compliant, and without personal data.
On which data is Prompt Decoding based?
Prompt Search Volume uses semantic reconstruction - estimating how often a given intent exists within the model's learned distribution. Think of it as the LLM equivalent of search volume.
Honest prompt volume estimates
We estimate prompt volume through semantic reconstruction, not a black-box panel number nobody can verify. If a tool claims exact real-world prompt volume, ask them to show their source. There is no public equivalent of Google keyword volume for ChatGPT prompts.
Cross-Model Consistency
Rankscale applies Prompt Decoding across ChatGPT and Gemini. Both models produce very similar clusters with this method and describe the same markets-so you get consistent, comparable insight across the engines that matter.
Methodological Basis
Rankscale uses Prompt Decoding based on internal model simulations in ChatGPT and Gemini. Typical questions, frames, and answer paths become visible: reproducible, privacy-compliant, and without personal data.
Why Use Prompt Research?
Rankscale delivers Prompt Research exclusively: scientifically reproduced, cross-model consistent, methodologically sound, and recognized by the professional community. Make LLM usage purposes visible without tracking real users.
Intents
Giving back strongest intents and clusters for a segment - understand what users are really asking AI engines about your market.
Prompts
Core prompts with trends - see which questions drive AI conversations in your space and how they evolve over time.
Entity
Entity Impression Share - measure how often your brand entity appears relative to competitors across AI engine responses.
“Finally an AI visibility tracking software that skips the fluff and shows the data underneath - instead of just shiny graphs without the ability to go deep on single queries, citations and sentiment.”
CEO & Founder, Zeitdice
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Frequently Asked Questions
Rankscale answers common questions about Prompt Research and Prompt Decoding.
Rankscale licenses Prompt Decoding, a method developed by Hanns Kronenberg to make the actual usage purposes of large language models visible. It is based on millions of real prompts and relies on internal model simulations in ChatGPT and Gemini-reproducible, privacy-compliant, and without personal data.
Still have questions? Contact our team