GSC to LLM Prompt Generator
Turn your Google Search Console queries into AI Search prompts.
Build a traceable prompt set from your own search queries. Review, edit and export it without uploading your data.
01 /Upload your data
Your CSV is processed locally in your browser. Your search queries and metrics are not uploaded to our servers.
CSV or tabular TXT · UTF-8 · comma, semicolon or tab
Google Search Console → Performance → Search results → Queries → Export → Download CSV. If the export is a ZIP, extract it and choose Queries.csv.
Every generated prompt is a hypothesis derived from a GSC query. It is not an observed ChatGPT request or evidence of demand in AI Search.
Synthetic demo: all queries and metrics are fictional. ExampleCRM and FictionalDesk are example brand terms. You can change the output language to try the Ukrainian queries.
Choose a CSV or try the demo.
02 /Check columns and preview
03 /Configure generation
Choose a CSV or try the demo to start. Your prompt set will appear here.
04 /Review your prompt set
Choose the prompt language, then regenerate the set. Source queries are not translated. Interface language is a separate setting.
Settings changed. Generate again to apply them; the current review and export still use the previous configuration.
- Source queries
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- Eligible queries
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- Generated candidates
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- Approved prompts
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- Covered impressions
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Source and eligible queries refer to the imported dataset and generation settings. The remaining KPIs use active review filters. Excluded prompts do not contribute coverage. Each exact source row is counted once, even across multiple prompts.
Coverage is a sum of distinct imported rows, not an LLM demand estimate or a reconstructed GSC property total. Rows with different metrics remain separate. Do not combine overlapping periods.
The candidate limit was reached. Narrow the source selection or generate fewer variants to cover more queries.
Filter the review
05 /Export a reusable prompt set
All exports respect the active review filters and exclude excluded prompts. CSV uses one row per prompt–source link, with duplicate record IDs grouped together. Repeated source metrics are provenance, not values to sum across prompts. JSON retains configuration, sources and links for reuse.
JSON exports all non-excluded prompts in the filtered view.
What is a GSC to LLM Prompt Generator?
This free tool turns imported Google Search Console queries into editable prompt hypotheses for AI Search research. Each candidate keeps its source query, original metrics, classification rule and template ID. You decide which prompts are worth testing.
How to export queries from Google Search Console
Open Performance → Search results, choose the date range and filters you need, and select the Queries tab. Export the data as CSV. If Google downloads a ZIP archive, extract it and choose Queries.csv here. Only the query column is required; you can map optional metrics and URLs before importing.
How search queries become AI Search prompts
The browser normalizes whitespace and Unicode, detects exact duplicate rows, applies intent rules and selects versioned English or Ukrainian templates. Topic groups are based on shared words; grouping does not merge metrics. Unknown, unsupported or cross-language inputs remain unchanged for manual review.
Search intent and prompt classification
Intent rules distinguish informational, how-to, comparison, commercial, transactional, navigational and troubleshooting queries. A query may have several plausible meanings; Unknown is a valid outcome. Brand detection uses your list or a domain in the query. You can override intent in View source.
How to build an AI Search prompt set
Start with the default top 200 queries and one candidate per query. Review coverage, adjust source limits and templates, edit unclear wording, approve useful candidates and exclude the rest. Filters apply to the editor and exports. The 500-candidate limit keeps review manageable; it is separate from the import limit.
Limitations of Google Search Console data
An ordinary CSV export can contain a limited subset of rows. Missing or anonymized queries cannot be recovered from the file. Imported impressions describe Google Search exposure, not the number of LLM requests. Blank metrics stay null. Exported zeros are preserved as provided; this tool cannot determine whether Google originally displayed an unavailable value. Query–page rows or overlapping slices may not add up to a property total.
Privacy and local processing
Parsing and generation run in a browser worker. No CSV, query, prompt, domain, metric or confidential setting is uploaded. There are no accounts, LLM API calls, dataset cookies or saved projects. Clear data removes application references and stops the worker. Reloading the page starts a fresh session; export work you want to keep.
Frequently Asked Questions
Are these real ChatGPT or Claude user queries?
No. They are hypothetical prompts derived from your GSC queries by local templates. Search keywords and conversational prompts are different kinds of input. A plausible prompt does not demonstrate actual demand or popularity in any LLM.
Can the generator check brand mentions or citations?
No. It does not execute prompts, inspect LLM answers or track citations. Those checks belong to a separate tool; the exported prompt set is a starting point for further research.
Does the CSV contain all of my search demand?
Not necessarily. Export limits, anonymized queries and the selected GSC filters can leave data out. This tool analyzes only what you provide and cannot reconstruct missing queries.
Can I use a file with no GSC metrics?
Yes. Map the query column and leave the other fields unmapped. Source order replaces impression-based ranking, and missing metrics remain null in JSON and blank in CSV.
Is my data sent to a server or translated by an AI?
No. Processing and export are local. English and Ukrainian templates are built in. The tool does not translate unsupported or cross-language queries; it marks them Needs review.