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

=== 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.