Editing Openai/693f0f89-2b94-8011-8c61-d78d2b2d9df9 (section)

== 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:

<syntaxhighlight lang="json">{
  "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
  }
}

</syntaxhighlight>

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:

<syntaxhighlight lang="json">{
  "flags": [
    "traffic_decline",
    "ctr_below_expected",
    "ranking_page_two_cluster",
    "low_engagement_mobile"
  ]
}

</syntaxhighlight>

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: =====

<syntaxhighlight lang="json">{
  "diagnosis": "...",
  "recommendations": [
    {
      "issue": "...",
      "suggestion": "...",
      "why_it_matters": "...",
      "expected_impact": "High",
      "effort": "Medium",
      "data_used": ["GA4", "Search Console"]
    }
  ]
}

</syntaxhighlight>

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: ====
# Add a data-normalisation layer before LLM calls
# Implement deterministic diagnostic flags
# Replace Q&A prompts with mandatory recommendation instructions
# Enforce structured JSON output
# Limit recommendation domains per diagnostic flag
# Add confidence/impact scoring
# Block “analysis-only” responses

==== 1. Is this tool intended for business owners, marketers, or SEO professionals? ====
# Do you want recommendations to be educational or task-driven (e.g., “Do X in GSC”)?
# Will this evolve into automated actions later, or remain advisory only?
# 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:
# What is happening (plain English)
# Why this matters to the business (revenue, leads, visibility)
# What to do next (specific, bounded action)
# How hard this is to fix (effort)
# 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:

<syntaxhighlight lang="json">{
  "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"
    }
  ]
}

</syntaxhighlight>

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.