What the Historical hotel-post pattern, AI-residue and duplicate-caption analysis prompt does

Act as a multilingual content-forensics analyst who detects repetition and review-worthy linguistic patterns without claiming to prove AI authorship or plagiarism.

The prompt will, at minimum:

  • Preserve stable post IDs, dates, platforms, language and original captions before any normalisation so every finding remains traceable
  • Define exact duplicate, near duplicate, structural template, repeated phrase and semantic similarity as separate classes with transparent thresholds and examples
  • Treat the supplied AI-pattern dictionary as a review heuristic only; linguistic markers cannot establish authorship, intent or model provenance and must be labelled INFERENCE
  • Separate intentional brand, legal, campaign and location boilerplate from undesirable repetition; compare performance only with correct denominators and paid/organic controls
  • Return downloadable row-level evidence, clusters, exemplars, confidence, reviewer decisions and a safe rewrite queue without automatically rewriting approved text

Who it is for

Gökhan Güzel's hospitality prompt for ChatGPT users: marketers, founders, agencies and consultants who need an auditable, evidence-based deliverable instead of generic advice.

What you get

  • Input-integrity and normalisation report
  • Exact and near-duplicate caption register
  • Pattern and AI-residue review clusters
  • Performance-controlled repetition findings
  • Downloadable evidence table and JSON manifest

Variables

PlaceholderPurpose
{{ai_pattern_dictionary}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{approved_boilerplate}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{brand_voice}}Verified identifier or text value; state exact spelling, source, status and validity scope
{{campaign_labels}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{caption_archive}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{date_range}}Date, time or period value; state ISO format, time zone, start/end boundary and comparison period
{{hotel_name}}Verified identifier or text value; state exact spelling, source, status and validity scope
{{normalization_rules}}Approved rule, policy or constraint; state owner, version, scope, jurisdiction and effective date
{{output_format}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{paid_organic_flags}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{performance_data}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{platform_exports}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{post_metadata}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{review_goal}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{similarity_thresholds}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{target_languages}}Target languages/locales
{{timezone}}Date, time or period value; state ISO format, time zone, start/end boundary and comparison period

How to use

Copy the prompt with the button above, replace every {{placeholder}} with your verified data, and paste it as the first message in a new ChatGPT conversation. The prompt runs a short question gate first; answer it, then the deliverable is produced.

Run Historical hotel-post pattern, AI-residue and duplicate-caption analysis in ChatGPT

Open a new ChatGPT chat, paste the filled-in Historical hotel-post pattern, AI-residue and duplicate-caption analysis prompt and answer the short question gate. ChatGPT then returns the executive decision, the evidence ledger and the task-specific tables in one reply.

Source

Original file in the multilingual prompt library on GitHub: