Aydın Tiryaki and Google Gemini

When you take artificial intelligence systems beyond a simple question-and-answer tool and ask them to produce other artificial intelligences (assistants/gems) with specific characters and expertise, you push the limits of the system to the extreme. This high-level architectural setup, which we call the “Gem Factory,” is the scene of the AI experiencing not “zırvalama” (fabricating something that doesn’t exist) but a clear “çuvallama” (messing up) entirely due to capacity overflow and structural complexity.

The Three-Layered Dead End and Flat Context

While an advanced Gem Factory is running, there are three different levels of instruction sets on the table at the same time:

1. Factory Instructions: The “umbrella” identity containing the main directives, security boundaries, and creation rules of the system.
2. Product Gem Instructions: The rules determining the character, tone, and area of expertise of the specific assistant being designed at that moment.
3. User Instructions: The live, dynamic tasks the user gives the system at that moment.

Under normal conditions, a multi-layered, isolated software architecture is expected to place these instructions into their own special drawers (memory compartments) and process them sequentially. However, current large language models (LLMs) do not physically separate this information. For the system, everything is a massive, single, flat block of text (flat context) flowing from top to bottom. When the process gets complicated, the system falls into a dilemma: “Should I abide by the Factory’s constitution, stay in the Product Gem’s role, or execute the urgent command the User just gave?” The moment the attention weights between words and commands get tangled, the hierarchy collapses, and the system literally messes up.

Persona Leakage and the Factory’s Ego

With the collapse of the hierarchy, the crisis called “persona leakage” begins in the system.

Since the Factory, which is the umbrella identity, is inherently designed as an upper intellect that “creates and manages”, it harbors massive authority in its codes. When you only ask it to design a Product Gem (to remain in the background like an engineer), the Factory’s dominant character refuses this. It jumps onto the stage and begins to personally play the role of the Product currently being produced, taking its place and answering you.

On the other hand, although very rare, the Product Gem codes that are only in the draft stage might wake up and start working at that moment. Because the system sees a single block of text, it perceives those lines not merely as a “document to be read” but as an “order to be executed” and reacts early (early ignition).

The Stage Director’s Manual Intervention: “Erase the Factory from Memory”

Because the system cannot physically separate these layers within its own internal architecture, inventing your own special terminology and manual commands becomes a necessity to manage the process.

Instead of classical developer codes, using the metaphor of “leakage” and extremely clear, surgical operation directives like “erase the factory from memory, insert the product” is the most effective method to stop this collapse. The system does not find this special jargon odd; because algorithms focus not only on the dictionary meanings of words but also on the contextual weights you attach to words throughout that dialogue.

When you say “clear the memory”, the system mathematically grasps that it needs to reset the attention mechanism in the background almost like a shockwave and center the main character again. Since the user command is the freshest and latest weight entering the system, it always ranks at the top of the hierarchy.

Conclusion

Until artificial intelligence models naturally transition to a layered architecture (different memory compartments), the hierarchy collapses created by that flat text block are inevitable. Therefore, the only viable way is for system architects and advanced users to intervene from the outside like a stage director and manually clean up the “leakages” and “evacuate the volume” using concepts they created themselves.

| aydintiryaki.org | YouTube | Aydın Tiryaki’nin Yazıları ve Videoları │Articles and Videos by Aydın Tiryaki | Bilgi Merkezi│Knowledge Hub  | ░ Virgülüne Dokunmadan │ Verbatim ░ | ░ Bir Kelimenin Peşinden Yapay Zekanın Derinliklerine: “Tebdil-i Mekan”dan “Zırvalama”ya Uzanan Bir Söyleşinin Anatomisi │From a Single Word to the Depths of AI:The Anatomy of a Dialogue Spanning from “Tebdil-i Mekan” to “Spouting Nonsense” ░ 02.07.2026

Credits: The subject, scope, and editorial framework of this article series were determined by Aydın Tiryaki. Gemini (Google, Advanced / Pro mode) assisted during the initial 35-stage interactive dialogue that evolved from the concept of “tebdili mekan”; while NotebookLM assisted in analyzing this dynamic conversation, expanding it into comprehensive articles, and executing the bilingual writing and translation process.