We keep seeing weird differences between ChatGPT, Gemini, and Perplexity when people ask about our product. I need a way to document those differences, track them by region if possible, and prove whether the issue is our content or the models. Any recommendations?

Yes — the easiest way to make this actionable is to set up a repeatable audit pipeline that captures:

1. What each model says
2. Where the query appears to originate from
3. Which source material the answer was likely based on
4. Whether the problem is your content, retrieval/indexing, or model behavior

Here’s a practical approach.

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1) Create a “model answer audit” template

For every prompt you care about, record:

• Date/time
• Model / product
  • ChatGPT
  • Gemini
  • Perplexity
• Mode
  • Web-enabled / RAG / browsing / plain chat
• Region / locale
  • Country
  • Language
  • If possible: IP region, UI locale, and browser language
• Prompt text
• Exact response
• Citations / links / sources shown
• Confidence / tone
• Outcome classification
  • Correct
  • Outdated
  • Missing
  • Hallucinated
  • Biased / inconsistent
  • Regionally different
• Suspected cause
  • Your website content
  • Structured data / schema
  • Third-party sources
  • Retrieval/indexing
  • Model generalization
  • Unknown

Use a spreadsheet or database at first; later you can automate it.

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2) Build a query set that reflects real user intent

You want a stable set of prompts that cover:

• Brand-level questions
  • “What does [product] do?”
  • “How much does [product] cost?”
  • “Is [product] available in [country]?”
• Feature questions
  • “Does [product] support SSO?”
  • “Can [product] integrate with Slack?”
• Comparison questions
  • “[Product] vs [competitor]”
• Policy / trust questions
  • “Is [product] secure?”
  • “Where is data stored?”
• Local availability
  • “Is [product] available in Germany?”
  • “Does [product] support VAT invoices in France?”

Keep the wording fixed so you can compare outputs over time.

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3) Capture region-specific behavior

If you want to track differences by region, test in a few controlled ways:

For browser-based products

Log:

• Browser locale
• Account country / signup country
• IP geolocation
• UI language
• Search engine region settings
• Device locale / timezone

For API-based testing

If you can’t directly control the model’s region, simulate locality by:

• Running tests from different cloud regions or VPN endpoints
• Setting language/locale in the prompt
• Using region-specific queries like “in the UK,” “for users in Brazil,” etc.

Important

Sometimes what looks like a “regional model difference” is actually one of these:

• Different search indexes
• Different web-crawling availability
• Geo-specific content on your site
• Country-specific third-party mentions
• Language translation issues

So track both the region and the source citations.

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4) Separate “content issue” from “model issue”

This is the key part.

A. Test your owned content directly

Check whether the answer is supported by:

• Your website
• Help docs
• Pricing page
• Product pages
• Structured data
• Knowledge base
• Public API docs
• Release notes

If the model answer is wrong but your content is ambiguous, stale, missing, or inconsistent, it’s probably a content problem.

B. Test the same prompt against multiple source conditions

Run the same question in these variants:

1. Your site updated and indexable
2. Your site hidden / noindex / blocked (if feasible in a test env)
3. Freshly updated content
4. Old content
5. Different locales/languages

If outputs change based on content presence, it points to retrieval/indexing or source quality, not just the model.

C. Compare with non-web and web-enabled modes

If a model without browsing gets one answer and the browsing/RAG version gets another, the issue is likely:

• retrieval
• ranking
• source selection
• citation grounding

If both are wrong in the same way, it may be:

• model prior knowledge
• prompt ambiguity
• product naming confusion
• weak public footprint

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5) Track citations and source provenance

For Gemini and Perplexity especially, citations matter a lot. For each response, note:

• Which domains were cited
• Whether your domain was included
• Whether citations support the exact claim
• Whether there were contradictory sources
• Whether the model cited outdated pages or summaries

A simple scoring system helps:

• Supported
• Partially supported
• Unsupported
• Contradicted by source

This is often the fastest way to prove the problem is not “the model” alone, but the evidence it found.

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6) Set up a periodic benchmark

Create a small benchmark suite, maybe 20–50 prompts, and run it:

• Weekly
• After major content changes
• After product launches
• After region-specific site changes
• After search engine/index updates

Store results historically so you can see:

• regressions
• improvements
• region drift
• model-version drift

This is especially useful when leadership asks, “Has this been fixed?”

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7) Recommended tooling

Lightweight start

• Google Sheets / Airtable / Notion
• Manual copy-paste of responses
• Tags for region, model, prompt, issue type

Better automation

• A script that:
  • sends prompts
  • stores responses
  • captures citations
  • timestamps results
  • records locale/IP region if possible
• Save outputs into:
  • Postgres / BigQuery / Airtable
  • S3 / blob storage for raw logs

Analysis

• Dashboards in:
  • Looker Studio
  • Metabase
  • Tableau
  • Power BI

Useful charts:

• Accuracy by model
• Accuracy by region
• Citation support rate
• Issue type over time
• Source-domain frequency

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8) A simple framework for proving root cause

When you find a bad answer, classify it like this:

If your content is wrong or unclear

• Fix the content
• Update schema / metadata
• Add explicit language
• Improve canonical pages

If your content is correct but not being used

• Improve crawlability / indexing
• Strengthen internal linking
• Add schema markup
• Ensure pages are accessible in target regions
• Confirm robots/canonical/hreflang settings

If the model misinterprets correct content

• Rewrite content to be more explicit
• Add FAQs
• Use tighter phrasing
• Publish authoritative source pages
• Increase consistency across pages

If the issue is only in one model/product

• Likely model behavior or product-specific retrieval
• Provide vendor-specific examples and citations
• File support / feedback with exact prompts and outputs

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9) What to include in a “difference report”

For each discrepancy, document:

• Prompt
• Model
• Region
• Output summary
• Source citations
• Ground truth from your docs
• Discrepancy type
• Severity
• Likely cause
• Recommended fix
• Whether this is reproducible

This makes it easier to hand off to content, SEO, product, or vendor support teams.

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10) My strongest recommendation

If you want something reliable and defensible, do this:

1. Create a fixed prompt benchmark
2. Run it across models and regions on a schedule
3. Store raw responses and citations
4. Score each answer against your source-of-truth content
5. Tag the likely failure point
6. Use that to drive content fixes and vendor escalation

That gives you evidence to say:

• “Our content is ambiguous”
• “The model is pulling the wrong source”
• “This is region-specific indexing”
• “This is a vendor-side inconsistency”

If you want, I can also give you:

• a spreadsheet template
• a database schema
• or a Python script design for automating these tests across ChatGPT, Gemini, and Perplexity.