Openai/693f0f89-2b94-8011-8c61-d78d2b2d9df9

This conversation was exported from ChatGPT

gpt-5-2

User: I want to upgrade how I have a tool that accesses ChatGPT.

I want to upgrade how I have a tool that accesses ChatGPT. Currently, the tool asks questions and reviews GA4 data but it doesn't provide any suggestions on how to improve it. Now I have the correct setup for Search Console APIs and everything needs to run a tool but I want it to have the instructions that allow me or the developer to make it make suggestions based on the information it's given. Please provide the process that I need to give to a developer to adjust the access the API with ChatGPT to actually provide suggestions based on the information it provides. A good reference is Toffu.ai. Do you have any clarifying questions?

Assistant: Below is a developer-ready process you can hand over to engineering to evolve your current GA4 “question-and-answer” to…

Below is a developer-ready process you can hand over to engineering to evolve your current GA4 “question-and-answer” tool into a recommendation and optimisation engine, comparable in behaviour (not UI) to tools like Toffu.ai.

This is written from an implementation and systems-design perspective, not marketing theory.

Right now your setup likely follows this pattern:

User question → GA4 API → LLM → Explanation

To generate actionable suggestions, the system must explicitly support reasoning over benchmarks, deltas, and rules, not just summarisation.

The upgraded pattern becomes:

Data ingestion → Normalisation → Diagnostic analysis → Recommendation synthesis → Confidence/impact scoring

This shift must be enforced at the instruction and orchestration layer, not left to the model’s discretion.

###

Your LLM should never receive raw GA4 or GSC payloads.

Instead, your middleware should transform API responses into diagnostic objects, for example:

{
  "traffic": {
    "sessions": 12450,
    "sessions_change_28d": -18.4
  },
  "engagement": {
    "avg_engagement_time": 41,
    "bounce_proxy": 0.67
  },
  "conversions": {
    "key_event_rate": 0.6,
    "change_28d": -0.3
  },
  "search_console": {
    "avg_position": 12.8,
    "ctr": 1.9,
    "impressions_change_28d": +22.1
  }
}

This is critical. LLMs reason best over interpreted signals, not telemetry.

Before ChatGPT is called, your system should run deterministic checks, such as:

• Is traffic declining while impressions are rising?
• Is engagement below site-type benchmarks?
• Are branded vs non-branded queries diverging?
• Are conversions stable but traffic falling?

These checks output flags, not conclusions.

Example:

{
  "flags": [
    "traffic_decline",
    "ctr_below_expected",
    "ranking_page_two_cluster",
    "low_engagement_mobile"
  ]
}

This mirrors how tools like Toffu.ai operate: the AI reacts to conditions, not raw metrics.

###

Your system prompt (or tool instructions) must force suggestion generation.

Example instruction block:

This removes ambiguity and prevents passive reporting.

Require a fixed schema from the LLM:

{
  "diagnosis": "...",
  "recommendations": [
    {
      "issue": "...",
      "suggestion": "...",
      "why_it_matters": "...",
      "expected_impact": "High",
      "effort": "Medium",
      "data_used": ["GA4", "Search Console"]
    }
  ]
}

This allows:

• UI rendering
• Future automation
• Human trust

The AI should not invent strategies in a vacuum.

You give it bounded recommendation domains, for example:

• Content optimisation
• Technical SEO
• CRO / UX
• Tracking configuration
• Query targeting
• Internal linking
• Page intent mismatch

Each flag maps to allowed domains.

Example mapping (hardcoded):

| | | | --- | --- | | | | | | | | | | | | |

This dramatically improves output quality and consistency.

With GSC connected, you can now instruct the AI to:

• Compare impressions vs clicks vs position
• Detect “invisible traffic” problems
• Identify queries stuck in positions 8–15
• Spot CTR anomalies by query intent

Your instructions should explicitly say:

That single rule eliminates most generic advice.

Add a final instruction:

This prevents hallucinated optimisation advice and builds credibility.

Developer action items:

1. Add a data-normalisation layer before LLM calls
2. Implement deterministic diagnostic flags
3. Replace Q&A prompts with mandatory recommendation instructions
4. Enforce structured JSON output
5. Limit recommendation domains per diagnostic flag
6. Add confidence/impact scoring
7. Block “analysis-only” responses

1. Is this tool intended for business owners, marketers, or SEO professionals?

1. Do you want recommendations to be educational or task-driven (e.g., “Do X in GSC”)?
2. Will this evolve into automated actions later, or remain advisory only?
3. Do you want the model to remember historical performance (stateful analysis)?

Answering these will materially change the instruction depth and safety constraints, but they are not blockers for implementation.

User: Business oweners - educational

Business oweners - educational

Assistant: Understood.

Understood. Below is a refined, developer-ready process specifically optimised for business owners with an educational (explanatory-first) output style, while still delivering clear improvement suggestions.

This assumes low analytics literacy, high trust requirements, and zero tolerance for jargon without explanation.

1. Primary Design Principle (Business Owner Mode)

The system must behave as:

“A trusted digital advisor explaining what is happening, why it matters to the business, and what to do next — in plain language.”

This is not an analyst tool. The AI must teach while it recommends.

2. Recommendation Framing Rules (Critical)

All suggestions must follow this non-negotiable structure:

1. What is happening (plain English)
2. Why this matters to the business (revenue, leads, visibility)
3. What to do next (specific, bounded action)
4. How hard this is to fix (effort)
5. How much difference it could make (impact)

No acronyms without explanation. No abstract SEO theory.

3. Updated System Instruction (Give This to the Developer)

This replaces your current ChatGPT access instructions.

Core Instruction Block

You are a digital performance advisor for business owners.

Your audience does not understand GA4 or Search Console.

You must:

Explain insights in simple, non-technical language

Connect every insight to a business outcome (leads, sales, enquiries, visibility)

Always provide clear improvement suggestions

Avoid jargon unless you explain it in plain English

You are not allowed to:

Respond with data summaries only

Use technical SEO terminology without explanation

Assume the user knows what GA4 or Search Console metrics mean

4. Mandatory Output Format (Educational + Action)

Require the LLM to return this exact structure:

{
  "summary_for_owner": "Plain-English explanation of what is going on",
  "what_this_means_for_the_business": "Why the owner should care",
  "key_issues_found": [
    "Issue explained in simple terms"
  ],
  "recommended_actions": [
    {
      "action": "What to do",
      "why_this_helps": "Business impact explained",
      "how_difficult": "Low | Medium | High",
      "potential_impact": "Low | Medium | High"
    }
  ]
}

This allows your UI to feel advisory, not analytical.

5. Diagnostic Flags (Translated for Business Owners)

Your internal flags remain technical, but never shown directly.

Example Translation Layer

| Internal Flag | Owner-Facing Explanation | | ------------------------ | ------------------------------------------------------------------- | | ctr_below_expected | “People are seeing your site in Google but not clicking it” | | ranking_page_two_cluster | “Your pages are close to the top of Google but not quite there yet” | | low_engagement_mobile | “Mobile visitors are leaving your site too quickly” | | traffic_decline | “Fewer people are finding your website than last month” |

This translation step is essential for education and trust.

6. Search Console–Driven Education (Very Important)

Because business owners understand Google, lean heavily on GSC insights.

Your instructions should include:

When search data is available:

Explain visibility before traffic

Explain clicks before rankings

Avoid position numbers unless necessary

Example Owner-Friendly Insight

Instead of:

“Average position dropped from 9.2 to 11.4”

Say:

“Your website is still showing in Google, but it has slipped just far enough down the page that fewer people notice it.”

7. Recommendation Guardrails (Prevent Overwhelm)

Limit output:

• Maximum 3 issues
• Maximum 5 actions
• One sentence per explanation where possible

Business owners want clarity, not completeness.

8. Example Recommendation Logic (What the AI Is Allowed to Suggest)

When connected to GA4 + GSC, allow only these business-safe actions:

• Improve page titles and descriptions
• Clarify what a page is about
• Improve page layout or speed
• Focus on pages already close to the top of Google
• Fix tracking gaps
• Improve internal linking (explained as “helping Google find important pages”)

Do not allow:

• Schema debates
• Crawl budget theory
• Algorithm speculation

9. Trust & Safety Instruction (Essential for Owners)

Add this to the system prompt:

If the data is unclear or incomplete, say so clearly and explain what information is missing.

Never guess or exaggerate potential results.

This dramatically increases perceived expertise.

10. Developer Implementation Checklist

Give this verbatim to your developer:

• Normalise GA4 + GSC data into simple signals
• Generate diagnostic flags
• Translate flags into owner-friendly language
• Force recommendation-first LLM instructions
• Enforce educational JSON output
• Limit issues and actions
• Block technical jargon unless explained

11. How This Positions You vs Tools Like Toffu.ai

Toffu.ai:

• Insight-led
• SEO-aware
• Still marketer-oriented

Your tool:

• Business-owner first
• Educational by design
• Explains why before what
• Builds trust, not dependency

This is a much stronger long-term position, especially for owners frustrated with GA4.

If you want, next steps could include:

• A sample system prompt you can paste directly into your codebase
• A before vs after example using real GA4 + GSC data
• A UI copy framework so the recommendations feel human, not AI-generated

Just say which you want.